Edited, memorised or added to reading list

on 24-Apr-2018 (Tue)

Do you want BuboFlash to help you learning these things? Click here to log in or create user.

#probability
In probability theory and statistics, the negative binomial distribution is a discrete probability distribution of the number of successes in a sequence of independent and identically distributed Bernoulli trials before a specified (non-random) number of failures (denoted r) occurs.

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

Negative binomial distribution - Wikipedia
) 2 ( p ) {\displaystyle {\frac {r}{(1-p)^{2}(p)}}} <span>In probability theory and statistics, the negative binomial distribution is a discrete probability distribution of the number of successes in a sequence of independent and identically distributed Bernoulli trials before a specified (non-random) number of failures (denoted r) occurs. For example, if we define a 1 as failure, all non-1s as successes, and we throw a dice repeatedly until the third time 1 appears (r = three failures), then the probability distribution




#probability
The negative binomial distribution also arises as a continuous mixture of Poisson distributions (i.e. a compound probability distribution) where the mixing distribution of the Poisson rate is a gamma distribution. That is, we can view the negative binomial as a Poisson(λ) distribution, where λ is itself a random variable, distributed as a gamma distribution with shape = r and scale θ = p/(1 − p) or correspondingly rate β = (1 − p)/p .

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

Negative binomial distribution - Wikipedia
) . {\displaystyle \operatorname {Poisson} (\lambda )=\lim _{r\to \infty }\operatorname {NB} \left(r,{\frac {\lambda }{\lambda +r}}\right).} Gamma–Poisson mixture[edit source] <span>The negative binomial distribution also arises as a continuous mixture of Poisson distributions (i.e. a compound probability distribution) where the mixing distribution of the Poisson rate is a gamma distribution. That is, we can view the negative binomial as a Poisson(λ) distribution, where λ is itself a random variable, distributed as a gamma distribution with shape = r and scale θ = p/(1 − p) or correspondingly rate β = (1 − p)/p. To display the intuition behind this statement, consider two independent Poisson processes, “Success” and “Failure”, with intensities p and 1 − p. Together, the Success and Failure pr




#probability
The negative binomial distribution also arises as a continuous mixture of Poisson distributions (i.e. a compound probability distribution) where the mixing distribution of the Poisson rate is a gamma distribution.

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on


Parent (intermediate) annotation

Open it
The negative binomial distribution also arises as a continuous mixture of Poisson distributions (i.e. a compound probability distribution) where the mixing distribution of the Poisson rate is a gamma distribution. That is, we can view the negative binomial as a Poisson(λ) distribution, where λ is itself a random variable, distributed as a gamma distribution with shape = r and scale θ = p/(1 − p)

Original toplevel document

Negative binomial distribution - Wikipedia
) . {\displaystyle \operatorname {Poisson} (\lambda )=\lim _{r\to \infty }\operatorname {NB} \left(r,{\frac {\lambda }{\lambda +r}}\right).} Gamma–Poisson mixture[edit source] <span>The negative binomial distribution also arises as a continuous mixture of Poisson distributions (i.e. a compound probability distribution) where the mixing distribution of the Poisson rate is a gamma distribution. That is, we can view the negative binomial as a Poisson(λ) distribution, where λ is itself a random variable, distributed as a gamma distribution with shape = r and scale θ = p/(1 − p) or correspondingly rate β = (1 − p)/p. To display the intuition behind this statement, consider two independent Poisson processes, “Success” and “Failure”, with intensities p and 1 − p. Together, the Success and Failure pr




Flashcard 1729703841036

Tags
#probability
Question
The negative binomial distribution also arises as a [...] of Poisson distributions
Answer
continuous mixture

Can be used to model over dispersed count observations, known as Gamma-Poisson distribution.


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
The negative binomial distribution also arises as a continuous mixture of Poisson distributions (i.e. a compound probability distribution) where the mixing distribution of the Poisson rate is a gamma distribution.

Original toplevel document

Negative binomial distribution - Wikipedia
) . {\displaystyle \operatorname {Poisson} (\lambda )=\lim _{r\to \infty }\operatorname {NB} \left(r,{\frac {\lambda }{\lambda +r}}\right).} Gamma–Poisson mixture[edit source] <span>The negative binomial distribution also arises as a continuous mixture of Poisson distributions (i.e. a compound probability distribution) where the mixing distribution of the Poisson rate is a gamma distribution. That is, we can view the negative binomial as a Poisson(λ) distribution, where λ is itself a random variable, distributed as a gamma distribution with shape = r and scale θ = p/(1 − p) or correspondingly rate β = (1 − p)/p. To display the intuition behind this statement, consider two independent Poisson processes, “Success” and “Failure”, with intensities p and 1 − p. Together, the Success and Failure pr







Flashcard 1729705413900

Tags
#probability
Question
[...] also arises as a continuous mixture as Gamma-Poisson distributions
Answer
The negative binomial distribution


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
The negative binomial distribution also arises as a continuous mixture of Poisson distributions (i.e. a compound probability distribution) where the mixing distribution of the Poisson rate is a gamma distribution. </spa

Original toplevel document

Negative binomial distribution - Wikipedia
) . {\displaystyle \operatorname {Poisson} (\lambda )=\lim _{r\to \infty }\operatorname {NB} \left(r,{\frac {\lambda }{\lambda +r}}\right).} Gamma–Poisson mixture[edit source] <span>The negative binomial distribution also arises as a continuous mixture of Poisson distributions (i.e. a compound probability distribution) where the mixing distribution of the Poisson rate is a gamma distribution. That is, we can view the negative binomial as a Poisson(λ) distribution, where λ is itself a random variable, distributed as a gamma distribution with shape = r and scale θ = p/(1 − p) or correspondingly rate β = (1 − p)/p. To display the intuition behind this statement, consider two independent Poisson processes, “Success” and “Failure”, with intensities p and 1 − p. Together, the Success and Failure pr







Flashcard 1729706986764

Tags
#probability
Question
[...] distribution is the number of successes in a sequence of iid Bernoulli trials before a specified number of failures (denoted r) occurs.
Answer
negative binomial distribution


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
In probability theory and statistics, the negative binomial distribution is a discrete probability distribution of the number of successes in a sequence of independent and identically distributed Bernoulli trials before a specified (non-random) number of fai

Original toplevel document

Negative binomial distribution - Wikipedia
) 2 ( p ) {\displaystyle {\frac {r}{(1-p)^{2}(p)}}} <span>In probability theory and statistics, the negative binomial distribution is a discrete probability distribution of the number of successes in a sequence of independent and identically distributed Bernoulli trials before a specified (non-random) number of failures (denoted r) occurs. For example, if we define a 1 as failure, all non-1s as successes, and we throw a dice repeatedly until the third time 1 appears (r = three failures), then the probability distribution







#computer-science #mathematics
In mathematics and computer science, a canonical, normal, or standard form of a mathematical object is a standard way of presenting that object as a mathematical expression.

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

Canonical form - Wikipedia
strings " madam curie " and " radium came " are given as C arrays. Each one is converted into a canonical form by sorting. Since both sorted strings literally agree, the original strings were anagrams of each other. <span>In mathematics and computer science, a canonical, normal, or standard form of a mathematical object is a standard way of presenting that object as a mathematical expression. The distinction between "canonical" and "normal" forms varies by subfield. In most fields, a canonical form specifies a unique representation for every object, while




Flashcard 1731649735948

Tags
#computer-science #mathematics
Question
a [...] form of a mathematical object is a standard way of presenting that object as a mathematical expression.
Answer
canonical, normal, or standard form


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
In mathematics and computer science, a canonical, normal, or standard form of a mathematical object is a standard way of presenting that object as a mathematical expression.

Original toplevel document

Canonical form - Wikipedia
strings " madam curie " and " radium came " are given as C arrays. Each one is converted into a canonical form by sorting. Since both sorted strings literally agree, the original strings were anagrams of each other. <span>In mathematics and computer science, a canonical, normal, or standard form of a mathematical object is a standard way of presenting that object as a mathematical expression. The distinction between "canonical" and "normal" forms varies by subfield. In most fields, a canonical form specifies a unique representation for every object, while







#linear-state-space-models

The objects in play are:

  • An \( n \times 1\) vector \(x_t\) denoting the state at time \(t = 0, 1, 2, \ldots\)
  • An iid sequence of \(m \times 1\) random vectors \(w_t \sim N(0,I)\)
  • A \(k \times 1\) vector \(y_t\) of observations at time \( t = 0, 1, 2, \ldots\)
  • An \(n \times n\) matrix A called the transition matrix
  • An \(n \times m\) matrix C called the volatility matrix
  • A \(k \times n\) matrix G sometimes called the output matrix

Here is the linear state-space system\(\) \begin{aligned} x_{t+1} & = A x_t + C w_{t+1} \\ y_t & = G x_t \nonumber \\ x_0 & \sim N(\mu_0, \Sigma_0) \nonumber \end{aligned}


.
.

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on




#linear-state-space-models

A martingale difference sequence is a sequence that is zero mean when conditioned on past information

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

Linear State Space Models – Quantitative Economics
Martingale difference shocks¶ We’ve made the common assumption that the shocks are independent standardized normal vectors But some of what we say will be valid under the assumption that {wt+1}{wt+1} is a martingale difference sequence <span>A martingale difference sequence is a sequence that is zero mean when conditioned on past information In the present case, since {xt}{xt} is our state sequence, this means that it satisfies 𝔼[wt+1|xt,xt−1,…]=0E[wt+1|xt,xt−1,…]=0 This is a weaker condition than that {wt}{wt} is i




#linear-state-space-models

The primitives of the model are

  1. the matrices A , C , G A,C,G A, C, G
  2. shock distribution, which we have specialized to N ( 0 , I ) N(0,I) N(0,I)
  3. the distribution of the initial condition x 0 x0 x_0 , which we have set to N ( μ 0 , Σ 0 )

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on




#linear-state-space-models
In state space models, finding the state is an art

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

Linear State Space Models – Quantitative Economics
N(0,I) Examples¶ By appropriate choice of the primitives, a variety of dynamics can be represented in terms of the linear state space model The following examples help to highlight this point They also illustrate the wise dictum <span>finding the state is an art Second-order difference equation¶ Let {yt}{yt} be a deterministic sequence that satifies (2)¶ yt+1=ϕ0+ϕ1yt+ϕ2yt−1s.t.y0,y−1 givenyt+1=ϕ0+ϕ1yt+ϕ2yt−1s.t.y0,y−1 given To map (2




#linear-state-space-models
Sufficient statistics form a list of objects that characterize a population distribution

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

Linear State Space Models – Quantitative Economics
full distribution However, there are some situations where these moments alone tell us all we need to know These are situations in which the mean vector and covariance matrix are sufficient statistics for the population distribution (<span>Sufficient statistics form a list of objects that characterize a population distribution) One such situation is when the vector in question is Gaussian (i.e., normally distributed) This is the case here, given our Gaussian assumptions on the primitives the fact that n




#linear-state-space-models
In linear state space models, we can generate independent draws of y_T by repeatedly simulating the evolution of the system up to time T , using an independent set of shocks each time

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on




#linear-state-space-models
The difference equation \( \mu_{t+1} = A \mu_t \) is known to have unique fixed point \( \mu_{\infty} = 0 \) if all eigenvalues of A have moduli strictly less than unity.

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on




#linear-state-space-models

However, global stability is more than we need for stationary solutions, and often more than we want

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

Linear State Space Models – Quantitative Economics
o has a unique fixed point in this case, and, moreover μt→μ∞=0andΣt→Σ∞ast→∞μt→μ∞=0andΣt→Σ∞ast→∞ regardless of the initial conditions μ0μ0 and Σ0Σ0 This is the globally stable case — see these notes for more a theoretical treatment <span>However, global stability is more than we need for stationary solutions, and often more than we want To illustrate, consider our second order difference equation example Here the state is xt=[1ytyt−1]′xt=[1ytyt−1]′ Because of the constant first component in the state vector, we w




#linear-state-space-models

Ergodicity is the property that time series and ensemble averages coincide

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

Linear State Space Models – Quantitative Economics
verages x¯:=1T∑t=1Txtandy¯:=1T∑t=1Tytx¯:=1T∑t=1Txtandy¯:=1T∑t=1Tyt Do these time series averages converge to something interpretable in terms of our basic state-space representation? The answer depends on something called ergodicity <span>Ergodicity is the property that time series and ensemble averages coincide More formally, ergodicity implies that time series sample averages converge to their expectation under the stationary distribution In particular, 1T∑Tt=1xt→μ∞1T∑t=1Txt→μ∞ 1T∑Tt=1(




#linear-state-space-models

More formally, ergodicity implies that time series sample averages converge to their expectation under the stationary distribution

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

Linear State Space Models – Quantitative Economics
hese time series averages converge to something interpretable in terms of our basic state-space representation? The answer depends on something called ergodicity Ergodicity is the property that time series and ensemble averages coincide <span>More formally, ergodicity implies that time series sample averages converge to their expectation under the stationary distribution In particular, 1T∑Tt=1xt→μ∞1T∑t=1Txt→μ∞ 1T∑Tt=1(xt−x¯T)(xt−x¯T)′→Σ∞1T∑t=1T(xt−x¯T)(xt−x¯T)′→Σ∞ 1T∑Tt=1(xt+j−x¯T)(xt−x¯T)′→AjΣ∞1T∑t=1T(xt+j−x¯T)(xt−x¯T)′→AjΣ∞ In our linear Gaussia




#linear-state-space-models

In our linear Gaussian setting, any covariance stationary process is also ergodic

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

Linear State Space Models – Quantitative Economics
ample averages converge to their expectation under the stationary distribution In particular, 1T∑Tt=1xt→μ∞1T∑t=1Txt→μ∞ 1T∑Tt=1(xt−x¯T)(xt−x¯T)′→Σ∞1T∑t=1T(xt−x¯T)(xt−x¯T)′→Σ∞ 1T∑Tt=1(xt+j−x¯T)(xt−x¯T)′→AjΣ∞1T∑t=1T(xt+j−x¯T)(xt−x¯T)′→AjΣ∞ <span>In our linear Gaussian setting, any covariance stationary process is also ergodic Noisy Observations¶ In some settings the observation equation yt=Gxtyt=Gxt is modified to include an error term Often this error term represents the idea that the true sta




#linear-state-space-models

The theory of prediction for linear state space systems is elegant and simple

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

Linear State Space Models – Quantitative Economics
t]=Gμt The variance-covariance matrix of ytyt is easily shown to be (19)¶ Var[yt]=Var[Gxt+Hvt]=GΣtG′+HH′Var[yt]=Var[Gxt+Hvt]=GΣtG′+HH′ The distribution of ytyt is therefore yt∼N(Gμt,GΣtG′+HH′)yt∼N(Gμt,GΣtG′+HH′) Prediction¶ <span>The theory of prediction for linear state space systems is elegant and simple Forecasting Formulas – Conditional Means¶ The natural way to predict variables is to use conditional distributions For example, the optimal forecast of xt+1xt+1 given informatio




#linear-state-space-models

The natural way to predict variables is to use conditional distributions

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

Linear State Space Models – Quantitative Economics
vt]=GΣtG′+HH′ The distribution of ytyt is therefore yt∼N(Gμt,GΣtG′+HH′)yt∼N(Gμt,GΣtG′+HH′) Prediction¶ The theory of prediction for linear state space systems is elegant and simple Forecasting Formulas – Conditional Means¶ <span>The natural way to predict variables is to use conditional distributions For example, the optimal forecast of xt+1xt+1 given information known at time tt is 𝔼t[xt+1]:=𝔼[xt+1∣xt,xt−1,…,x0]=AxtEt[xt+1]:=E[xt+1∣xt,xt−1,…,x0]=Axt The right-hand side foll




#linear-state-space-models
if \( \{y_t\}\) is a stream of dividends, then \( \mathbb{E} \left[\sum_{j=0}^\infty \beta^j y_{t+j} | x_t \right]\) is a model of a stock price

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on




Flashcard 1737413758220

Tags
#linear-state-space-models
Question
In state space models, finding [...] is an art
Answer
the state


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
In state space models, finding the state is an art

Original toplevel document

Linear State Space Models – Quantitative Economics
N(0,I) Examples¶ By appropriate choice of the primitives, a variety of dynamics can be represented in terms of the linear state space model The following examples help to highlight this point They also illustrate the wise dictum <span>finding the state is an art Second-order difference equation¶ Let {yt}{yt} be a deterministic sequence that satifies (2)¶ yt+1=ϕ0+ϕ1yt+ϕ2yt−1s.t.y0,y−1 givenyt+1=ϕ0+ϕ1yt+ϕ2yt−1s.t.y0,y−1 given To map (2







Flashcard 1737419525388

Tags
#linear-state-space-models
Question
if \( \{y_t\}\) is [...], then \( \mathbb{E} \left[\sum_{j=0}^\infty \beta^j y_{t+j} | x_t \right]\) is a model of a stock price
Answer
a stream of dividends


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
if \( \{y_t\}\) is a stream of dividends, then \( \mathbb{E} \left[\sum_{j=0}^\infty \beta^j y_{t+j} | x_t \right]\) is a model of a stock price






Flashcard 1737421098252

Tags
#linear-state-space-models
Question
if \( \{y_t\}\) is a stream of dividends, then \( \mathbb{E} \left[\sum_{j=0}^\infty \beta^j y_{t+j} | x_t \right]\) is a model of [...]
Answer
a stock price


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
if \( \{y_t\}\) is a stream of dividends, then \( \mathbb{E} \left[\sum_{j=0}^\infty \beta^j y_{t+j} | x_t \right]\) is a model of a stock price






Flashcard 1737422671116

Tags
#linear-state-space-models
Question
if \( \{y_t\}\) is a stream of dividends, then [...] is a model of a stock price
Answer
\( \mathbb{E} \left[\sum_{j=0}^\infty \beta^j y_{t+j} | x_t \right]\)


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
if \( \{y_t\}\) is a stream of dividends, then \( \mathbb{E} \left[\sum_{j=0}^\infty \beta^j y_{t+j} | x_t \right]\) is a model of a stock price






Flashcard 1737424243980

Tags
#linear-state-space-models
Question

The natural way to predict variables is to use

[...]

Answer
conditional distributions


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
The natural way to predict variables is to use conditional distributions

Original toplevel document

Linear State Space Models – Quantitative Economics
vt]=GΣtG′+HH′ The distribution of ytyt is therefore yt∼N(Gμt,GΣtG′+HH′)yt∼N(Gμt,GΣtG′+HH′) Prediction¶ The theory of prediction for linear state space systems is elegant and simple Forecasting Formulas – Conditional Means¶ <span>The natural way to predict variables is to use conditional distributions For example, the optimal forecast of xt+1xt+1 given information known at time tt is 𝔼t[xt+1]:=𝔼[xt+1∣xt,xt−1,…,x0]=AxtEt[xt+1]:=E[xt+1∣xt,xt−1,…,x0]=Axt The right-hand side foll







Flashcard 1737427389708

Tags
#linear-state-space-models
Question

In our linear Gaussian setting, any covariance stationary process is also [...]

Answer
ergodic


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
In our linear Gaussian setting, any covariance stationary process is also ergodic

Original toplevel document

Linear State Space Models – Quantitative Economics
ample averages converge to their expectation under the stationary distribution In particular, 1T∑Tt=1xt→μ∞1T∑t=1Txt→μ∞ 1T∑Tt=1(xt−x¯T)(xt−x¯T)′→Σ∞1T∑t=1T(xt−x¯T)(xt−x¯T)′→Σ∞ 1T∑Tt=1(xt+j−x¯T)(xt−x¯T)′→AjΣ∞1T∑t=1T(xt+j−x¯T)(xt−x¯T)′→AjΣ∞ <span>In our linear Gaussian setting, any covariance stationary process is also ergodic Noisy Observations¶ In some settings the observation equation yt=Gxtyt=Gxt is modified to include an error term Often this error term represents the idea that the true sta







Flashcard 1737428962572

Tags
#linear-state-space-models
Question

In our linear Gaussian setting, any [...] process is also ergodic

Answer
covariance stationary


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
In our linear Gaussian setting, any covariance stationary process is also ergodic

Original toplevel document

Linear State Space Models – Quantitative Economics
ample averages converge to their expectation under the stationary distribution In particular, 1T∑Tt=1xt→μ∞1T∑t=1Txt→μ∞ 1T∑Tt=1(xt−x¯T)(xt−x¯T)′→Σ∞1T∑t=1T(xt−x¯T)(xt−x¯T)′→Σ∞ 1T∑Tt=1(xt+j−x¯T)(xt−x¯T)′→AjΣ∞1T∑t=1T(xt+j−x¯T)(xt−x¯T)′→AjΣ∞ <span>In our linear Gaussian setting, any covariance stationary process is also ergodic Noisy Observations¶ In some settings the observation equation yt=Gxtyt=Gxt is modified to include an error term Often this error term represents the idea that the true sta







Flashcard 1737430535436

Tags
#linear-state-space-models
Question

In [...] setting, any covariance stationary process is also ergodic

Answer
our linear Gaussian


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
In our linear Gaussian setting, any covariance stationary process is also ergodic

Original toplevel document

Linear State Space Models – Quantitative Economics
ample averages converge to their expectation under the stationary distribution In particular, 1T∑Tt=1xt→μ∞1T∑t=1Txt→μ∞ 1T∑Tt=1(xt−x¯T)(xt−x¯T)′→Σ∞1T∑t=1T(xt−x¯T)(xt−x¯T)′→Σ∞ 1T∑Tt=1(xt+j−x¯T)(xt−x¯T)′→AjΣ∞1T∑t=1T(xt+j−x¯T)(xt−x¯T)′→AjΣ∞ <span>In our linear Gaussian setting, any covariance stationary process is also ergodic Noisy Observations¶ In some settings the observation equation yt=Gxtyt=Gxt is modified to include an error term Often this error term represents the idea that the true sta







Flashcard 1737432108300

Tags
#linear-state-space-models
Question

More formally, ergodicity implies that [...] converge to their expectation under the stationary distribution

Answer
time series sample averages


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
More formally, ergodicity implies that time series sample averages converge to their expectation under the stationary distribution

Original toplevel document

Linear State Space Models – Quantitative Economics
hese time series averages converge to something interpretable in terms of our basic state-space representation? The answer depends on something called ergodicity Ergodicity is the property that time series and ensemble averages coincide <span>More formally, ergodicity implies that time series sample averages converge to their expectation under the stationary distribution In particular, 1T∑Tt=1xt→μ∞1T∑t=1Txt→μ∞ 1T∑Tt=1(xt−x¯T)(xt−x¯T)′→Σ∞1T∑t=1T(xt−x¯T)(xt−x¯T)′→Σ∞ 1T∑Tt=1(xt+j−x¯T)(xt−x¯T)′→AjΣ∞1T∑t=1T(xt+j−x¯T)(xt−x¯T)′→AjΣ∞ In our linear Gaussia







Flashcard 1737433681164

Tags
#linear-state-space-models
Question

More formally, ergodicity implies that time series sample averages converge to

[...]

Answer
their expectation under the stationary distribution


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
More formally, ergodicity implies that time series sample averages converge to their expectation under the stationary distribution

Original toplevel document

Linear State Space Models – Quantitative Economics
hese time series averages converge to something interpretable in terms of our basic state-space representation? The answer depends on something called ergodicity Ergodicity is the property that time series and ensemble averages coincide <span>More formally, ergodicity implies that time series sample averages converge to their expectation under the stationary distribution In particular, 1T∑Tt=1xt→μ∞1T∑t=1Txt→μ∞ 1T∑Tt=1(xt−x¯T)(xt−x¯T)′→Σ∞1T∑t=1T(xt−x¯T)(xt−x¯T)′→Σ∞ 1T∑Tt=1(xt+j−x¯T)(xt−x¯T)′→AjΣ∞1T∑t=1T(xt+j−x¯T)(xt−x¯T)′→AjΣ∞ In our linear Gaussia







Flashcard 1737435254028

Tags
#linear-state-space-models
Question

Ergodicity is the property that averages of [...] and [...] coincide

Answer
time series and ensemble

The ensemble is the samples from the stationary distribution


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Ergodicity is the property that time series and ensemble averages coincide

Original toplevel document

Linear State Space Models – Quantitative Economics
verages x¯:=1T∑t=1Txtandy¯:=1T∑t=1Tytx¯:=1T∑t=1Txtandy¯:=1T∑t=1Tyt Do these time series averages converge to something interpretable in terms of our basic state-space representation? The answer depends on something called ergodicity <span>Ergodicity is the property that time series and ensemble averages coincide More formally, ergodicity implies that time series sample averages converge to their expectation under the stationary distribution In particular, 1T∑Tt=1xt→μ∞1T∑t=1Txt→μ∞ 1T∑Tt=1(







Flashcard 1737437613324

Tags
#linear-state-space-models
Question

However, [...] is more than we need for stationary solutions, and often more than we want

Answer
global stability


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
However, global stability is more than we need for stationary solutions, and often more than we want

Original toplevel document

Linear State Space Models – Quantitative Economics
o has a unique fixed point in this case, and, moreover μt→μ∞=0andΣt→Σ∞ast→∞μt→μ∞=0andΣt→Σ∞ast→∞ regardless of the initial conditions μ0μ0 and Σ0Σ0 This is the globally stable case — see these notes for more a theoretical treatment <span>However, global stability is more than we need for stationary solutions, and often more than we want To illustrate, consider our second order difference equation example Here the state is xt=[1ytyt−1]′xt=[1ytyt−1]′ Because of the constant first component in the state vector, we w







Flashcard 1737441021196

Tags
#linear-state-space-models
Question
In linear state space models, we can generate independent draws of y_T by [...] , using an independent set of shocks each time
Answer
repeatedly simulating the evolution of the system up to time T


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
In linear state space models, we can generate independent draws of y_T by repeatedly simulating the evolution of the system up to time T , using an independent set of shocks each time






Flashcard 1737442594060

Tags
#linear-state-space-models
Question
In linear state space models, we can generate independent draws of y_T by repeatedly simulating the evolution of the system up to time T , using [...]
Answer
an independent set of shocks each time


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
In linear state space models, we can generate independent draws of y_T by repeatedly simulating the evolution of the system up to time T , using an independent set of shocks each time






Flashcard 1737444166924

Tags
#linear-state-space-models
Question
[...] form a list of objects that characterize a population distribution
Answer
Sufficient statistics


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Sufficient statistics form a list of objects that characterize a population distribution

Original toplevel document

Linear State Space Models – Quantitative Economics
full distribution However, there are some situations where these moments alone tell us all we need to know These are situations in which the mean vector and covariance matrix are sufficient statistics for the population distribution (<span>Sufficient statistics form a list of objects that characterize a population distribution) One such situation is when the vector in question is Gaussian (i.e., normally distributed) This is the case here, given our Gaussian assumptions on the primitives the fact that n







Flashcard 1737450720524

Tags
#linear-state-space-models
Question

In the linear state-space system\(\)
\begin{aligned} x_{t+1} & = A x_t + C w_{t+1} \\ y_t & = G x_t \nonumber \\ x_0 & \sim N(\mu_0, \Sigma_0) \nonumber \end{aligned}

C is called the [...]

Answer
volatility matrix


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
An n×1 vector xt denoting the state at time t=0,1,2,… An iid sequence of m×1 random vectors wt∼N(0,I) A k×1 vector yt of observations at time t=0,1,2,… An n×n matrix A called the transition matrix An n×m matrix C called the <span>volatility matrix A k×n matrix G sometimes called the output matrix Here is the linear state-space system xt+1ytx0=Axt+Cwt+1=Gxt∼N(μ0,Σ0) . .






Flashcard 1737453341964

Tags
#linear-state-space-models
Question
The difference equation \( \mu_{t+1} = A \mu_t \) is known to have unique fixed point \( \mu_{\infty} = 0 \) if [...].
Answer
all eigenvalues of A have moduli strictly less than unity


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
The difference equation μt+1=Aμt is known to have unique fixed point μ∞=0 if all eigenvalues of A have moduli strictly less than unity.






Flashcard 1737454914828

Tags
#linear-state-space-models
Question
the [...] distribution is specialized to \(N(0,I)\)
Answer
shock


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
The primitives of the model are the matrices A , C , G A,C,G A, C, G shock distribution, which we have specialized to N ( 0 , I ) N(0,I) N(0,I) the distribution of the initial condition x 0 x0 x_0 , which we have set to N ( μ 0 , Σ 0 )






Flashcard 1767820102924

Tags
#linear-state-space-models
Question

The theory of [...] for linear state space systems is elegant and simple

Answer
prediction


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
The theory of prediction for linear state space systems is elegant and simple

Original toplevel document

Linear State Space Models – Quantitative Economics
t]=Gμt The variance-covariance matrix of ytyt is easily shown to be (19)¶ Var[yt]=Var[Gxt+Hvt]=GΣtG′+HH′Var[yt]=Var[Gxt+Hvt]=GΣtG′+HH′ The distribution of ytyt is therefore yt∼N(Gμt,GΣtG′+HH′)yt∼N(Gμt,GΣtG′+HH′) Prediction¶ <span>The theory of prediction for linear state space systems is elegant and simple Forecasting Formulas – Conditional Means¶ The natural way to predict variables is to use conditional distributions For example, the optimal forecast of xt+1xt+1 given informatio







Flashcard 1799070813452

[unknown IMAGE 1799065308428]
Tags
#has-images #jaynes #plausible-reasoning
Question
Given
A ≡ it will start to rain by 10 am at the latest;
B ≡ the sky will become cloudy before 10 am.

The weaker syllogism (B is true) takes the form [...]


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

pdf

cannot see any pdfs







Flashcard 1802947398924

Question
Reflecting on the history of logic forces us to reflect on what it means to be a [...], to think properly.
Answer
reasonable cognitive agent


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Reflecting on the history of logic forces us to reflect on what it means to be a reasonable cognitive agent, to think properly.

Original toplevel document

Pocket: Log In
dgeman The history of logic should be of interest to anyone with aspirations to thinking that is correct, or at least reasonable. This story illustrates different approaches to intellectual enquiry and human cognition more generally. <span>Reflecting on the history of logic forces us to reflect on what it means to be a reasonable cognitive agent, to think properly. Is it to engage in discussions with others? Is it to think for ourselves? Is it to perform calculations? In the Critique of Pure Reason (1781), Immanuel Kant stated that no progress in







语料库的地址是: http:// corpus.byu.edu/coca/

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

Unknown title
英语 话题 的优秀回答者 564人赞了该文章 美国当代英语语料库(Corpus of Contemporary American English,简称COCA)是目前最大的免费英语语言库,它由包含5.2亿词的文本构成,这些文本由口语,小说,流行杂志,报纸以及学术文章五种不同的文体构成。从1990年至2015年年间语料库以每年增加两千万词的速度进行扩充,以保证语料库内容的时效性。因此,美国当代英语语料库被认为是用来观察美国英语当前发展变化的最合适的英语语料库。 <span>语料库的地址是: http:// corpus.byu.edu/coca/ 与传统词典相比,COCA具有以下几点优势: (1)语料库的文本实时性比较强,类似生活满意度,社交媒体这样的词很多传统词典都没有收录,但在语料库中都可以查到。 (2)语料库可以提供单词的词频信息,这有助于我们了解该单词在实际应用中的出现频率,有助于实现准确用词。 (3)语料库还能提供模糊搜索和单词搭配等功能。 。实际使用时可以将语料库作为词典的补充工具,在词典里面无法确




#linguistics #verbs
Aspect is a grammatical category that expresses how an action, event, or state, denoted by a verb, extends over time. Perfective aspect is used in referring to an event conceived as bounded and unitary, without reference to any flow of time during ("I helped him"). Imperfective aspect is used for situations conceived as existing continuously or repetitively as time flows ("I was helping him"; "I used to help people").

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

Grammatical aspect - Wikipedia
onstruction Singulative-Collective-Pluractive Specificity Subject/Object Suffixaufnahme (Case stacking) Tense Tense–aspect–mood Telicity Transitivity Topic and Comment Thematic relation (Agent/Patient) Valency Voice Volition v t e <span>Aspect is a grammatical category that expresses how an action, event, or state, denoted by a verb, extends over time. Perfective aspect is used in referring to an event conceived as bounded and unitary, without reference to any flow of time during ("I helped him"). Imperfective aspect is used for situations conceived as existing continuously or repetitively as time flows ("I was helping him"; "I used to help people"). Further distinctions can be made, for example, to distinguish states and ongoing actions (continuous and progressive aspects) from repetitive actions (habitual aspect). Certain aspe




#linguistics #verbs
mood is the use of verbal inflections that allow speakers to express their attitude toward what they are saying (e.g. a statement of fact, of desire, of command, etc.).

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

Grammatical mood - Wikipedia
onstruction Singulative-Collective-Pluractive Specificity Subject/Object Suffixaufnahme (Case stacking) Tense Tense–aspect–mood Telicity Transitivity Topic and Comment Thematic relation (Agent/Patient) Valency Voice Volition v t e <span>In linguistics, grammatical mood (also mode) is a grammatical feature of verbs, used for signaling modality. [2] [3] :p.181; [4] That is, it is the use of verbal inflections that allow speakers to express their attitude toward what they are saying (e.g. a statement of fact, of desire, of command, etc.). The term is also used more broadly to describe the syntactic expression of modality, that is, the use of verb phrases that do not involve inflexion of the verb itself. Mood is distinc




Flashcard 1803021847820

Tags
#linguistics #verbs
Question
mood is the use of [...] that allow speakers to express their attitude toward what they are saying (e.g. a statement of fact, of desire, of command, etc.).
Answer


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
mood is the use of verbal inflections that allow speakers to express their attitude toward what they are saying (e.g. a statement of fact, of desire, of command, etc.).

Original toplevel document

Grammatical mood - Wikipedia
onstruction Singulative-Collective-Pluractive Specificity Subject/Object Suffixaufnahme (Case stacking) Tense Tense–aspect–mood Telicity Transitivity Topic and Comment Thematic relation (Agent/Patient) Valency Voice Volition v t e <span>In linguistics, grammatical mood (also mode) is a grammatical feature of verbs, used for signaling modality. [2] [3] :p.181; [4] That is, it is the use of verbal inflections that allow speakers to express their attitude toward what they are saying (e.g. a statement of fact, of desire, of command, etc.). The term is also used more broadly to describe the syntactic expression of modality, that is, the use of verb phrases that do not involve inflexion of the verb itself. Mood is distinc







Flashcard 1803023420684

Tags
#linguistics #verbs
Question
[...] uses verbal inflections to allow speakers to express their attitude toward what they are saying
Answer
mood


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
mood is the use of verbal inflections that allow speakers to express their attitude toward what they are saying (e.g. a statement of fact, of desire, of command, etc.).

Original toplevel document

Grammatical mood - Wikipedia
onstruction Singulative-Collective-Pluractive Specificity Subject/Object Suffixaufnahme (Case stacking) Tense Tense–aspect–mood Telicity Transitivity Topic and Comment Thematic relation (Agent/Patient) Valency Voice Volition v t e <span>In linguistics, grammatical mood (also mode) is a grammatical feature of verbs, used for signaling modality. [2] [3] :p.181; [4] That is, it is the use of verbal inflections that allow speakers to express their attitude toward what they are saying (e.g. a statement of fact, of desire, of command, etc.). The term is also used more broadly to describe the syntactic expression of modality, that is, the use of verb phrases that do not involve inflexion of the verb itself. Mood is distinc







#linguistics #verbs
Aspect expresses how an action, event extends over time.

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on


Parent (intermediate) annotation

Open it
Aspect is a grammatical category that expresses how an action, event, or state, denoted by a verb, extends over time. Perfective aspect is used in referring to an event conceived as bounded and unitary, without reference to any flow of time during ("I helped him"). Imperfective aspect is used

Original toplevel document

Grammatical aspect - Wikipedia
onstruction Singulative-Collective-Pluractive Specificity Subject/Object Suffixaufnahme (Case stacking) Tense Tense–aspect–mood Telicity Transitivity Topic and Comment Thematic relation (Agent/Patient) Valency Voice Volition v t e <span>Aspect is a grammatical category that expresses how an action, event, or state, denoted by a verb, extends over time. Perfective aspect is used in referring to an event conceived as bounded and unitary, without reference to any flow of time during ("I helped him"). Imperfective aspect is used for situations conceived as existing continuously or repetitively as time flows ("I was helping him"; "I used to help people"). Further distinctions can be made, for example, to distinguish states and ongoing actions (continuous and progressive aspects) from repetitive actions (habitual aspect). Certain aspe




#linguistics #verbs
Perfective aspect refers to an event conceived as bounded and unitary, without reference to any flow of time during ("I helped him")

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on


Parent (intermediate) annotation

Open it
Aspect is a grammatical category that expresses how an action, event, or state, denoted by a verb, extends over time. Perfective aspect is used in referring to an event conceived as bounded and unitary, without reference to any flow of time during ("I helped him"). Imperfective aspect is used for situations conceived as existing continuously or repetitively as time flows ("I was helping him"; "I used to help people").

Original toplevel document

Grammatical aspect - Wikipedia
onstruction Singulative-Collective-Pluractive Specificity Subject/Object Suffixaufnahme (Case stacking) Tense Tense–aspect–mood Telicity Transitivity Topic and Comment Thematic relation (Agent/Patient) Valency Voice Volition v t e <span>Aspect is a grammatical category that expresses how an action, event, or state, denoted by a verb, extends over time. Perfective aspect is used in referring to an event conceived as bounded and unitary, without reference to any flow of time during ("I helped him"). Imperfective aspect is used for situations conceived as existing continuously or repetitively as time flows ("I was helping him"; "I used to help people"). Further distinctions can be made, for example, to distinguish states and ongoing actions (continuous and progressive aspects) from repetitive actions (habitual aspect). Certain aspe




#linguistics #verbs
Imperfective aspect is used for situations conceived as existing continuously or repetitively as time flows ("I was helping him"; "I used to help people").

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on


Parent (intermediate) annotation

Open it
that expresses how an action, event, or state, denoted by a verb, extends over time. Perfective aspect is used in referring to an event conceived as bounded and unitary, without reference to any flow of time during ("I helped him"). <span>Imperfective aspect is used for situations conceived as existing continuously or repetitively as time flows ("I was helping him"; "I used to help people"). <span><body><html>

Original toplevel document

Grammatical aspect - Wikipedia
onstruction Singulative-Collective-Pluractive Specificity Subject/Object Suffixaufnahme (Case stacking) Tense Tense–aspect–mood Telicity Transitivity Topic and Comment Thematic relation (Agent/Patient) Valency Voice Volition v t e <span>Aspect is a grammatical category that expresses how an action, event, or state, denoted by a verb, extends over time. Perfective aspect is used in referring to an event conceived as bounded and unitary, without reference to any flow of time during ("I helped him"). Imperfective aspect is used for situations conceived as existing continuously or repetitively as time flows ("I was helping him"; "I used to help people"). Further distinctions can be made, for example, to distinguish states and ongoing actions (continuous and progressive aspects) from repetitive actions (habitual aspect). Certain aspe




Flashcard 1803033382156

Tags
#linguistics #verbs
Question
[...] expresses how an action, event extends over time.
Answer
Aspect


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Aspect expresses how an action, event extends over time.

Original toplevel document

Grammatical aspect - Wikipedia
onstruction Singulative-Collective-Pluractive Specificity Subject/Object Suffixaufnahme (Case stacking) Tense Tense–aspect–mood Telicity Transitivity Topic and Comment Thematic relation (Agent/Patient) Valency Voice Volition v t e <span>Aspect is a grammatical category that expresses how an action, event, or state, denoted by a verb, extends over time. Perfective aspect is used in referring to an event conceived as bounded and unitary, without reference to any flow of time during ("I helped him"). Imperfective aspect is used for situations conceived as existing continuously or repetitively as time flows ("I was helping him"; "I used to help people"). Further distinctions can be made, for example, to distinguish states and ongoing actions (continuous and progressive aspects) from repetitive actions (habitual aspect). Certain aspe







Flashcard 1803034955020

Tags
#linguistics #verbs
Question
Aspect expresses how an action, event extends over [...].
Answer
time


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Aspect expresses how an action, event extends over time.

Original toplevel document

Grammatical aspect - Wikipedia
onstruction Singulative-Collective-Pluractive Specificity Subject/Object Suffixaufnahme (Case stacking) Tense Tense–aspect–mood Telicity Transitivity Topic and Comment Thematic relation (Agent/Patient) Valency Voice Volition v t e <span>Aspect is a grammatical category that expresses how an action, event, or state, denoted by a verb, extends over time. Perfective aspect is used in referring to an event conceived as bounded and unitary, without reference to any flow of time during ("I helped him"). Imperfective aspect is used for situations conceived as existing continuously or repetitively as time flows ("I was helping him"; "I used to help people"). Further distinctions can be made, for example, to distinguish states and ongoing actions (continuous and progressive aspects) from repetitive actions (habitual aspect). Certain aspe







Flashcard 1803037314316

Tags
#linguistics #verbs
Question
Perfective aspect refers to an event conceived as [...]
Answer
bounded and unitary


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Perfective aspect refers to an event conceived as bounded and unitary, without reference to any flow of time during ("I helped him")

Original toplevel document

Grammatical aspect - Wikipedia
onstruction Singulative-Collective-Pluractive Specificity Subject/Object Suffixaufnahme (Case stacking) Tense Tense–aspect–mood Telicity Transitivity Topic and Comment Thematic relation (Agent/Patient) Valency Voice Volition v t e <span>Aspect is a grammatical category that expresses how an action, event, or state, denoted by a verb, extends over time. Perfective aspect is used in referring to an event conceived as bounded and unitary, without reference to any flow of time during ("I helped him"). Imperfective aspect is used for situations conceived as existing continuously or repetitively as time flows ("I was helping him"; "I used to help people"). Further distinctions can be made, for example, to distinguish states and ongoing actions (continuous and progressive aspects) from repetitive actions (habitual aspect). Certain aspe







Flashcard 1803039673612

Tags
#linguistics #verbs
Question
Perfective aspect doesn't refer to any [...]
Answer
duration of time
("I helped him")


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Perfective aspect refers to an event conceived as bounded and unitary, without reference to any flow of time during ("I helped him")

Original toplevel document

Grammatical aspect - Wikipedia
onstruction Singulative-Collective-Pluractive Specificity Subject/Object Suffixaufnahme (Case stacking) Tense Tense–aspect–mood Telicity Transitivity Topic and Comment Thematic relation (Agent/Patient) Valency Voice Volition v t e <span>Aspect is a grammatical category that expresses how an action, event, or state, denoted by a verb, extends over time. Perfective aspect is used in referring to an event conceived as bounded and unitary, without reference to any flow of time during ("I helped him"). Imperfective aspect is used for situations conceived as existing continuously or repetitively as time flows ("I was helping him"; "I used to help people"). Further distinctions can be made, for example, to distinguish states and ongoing actions (continuous and progressive aspects) from repetitive actions (habitual aspect). Certain aspe







Flashcard 1803041246476

Tags
#linguistics #verbs
Question
[...] refers to an event conceived as bounded and unitary, without reference to any flow of time during ("I helped him")
Answer
Perfective aspect


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Perfective aspect refers to an event conceived as bounded and unitary, without reference to any flow of time during ("I helped him")

Original toplevel document

Grammatical aspect - Wikipedia
onstruction Singulative-Collective-Pluractive Specificity Subject/Object Suffixaufnahme (Case stacking) Tense Tense–aspect–mood Telicity Transitivity Topic and Comment Thematic relation (Agent/Patient) Valency Voice Volition v t e <span>Aspect is a grammatical category that expresses how an action, event, or state, denoted by a verb, extends over time. Perfective aspect is used in referring to an event conceived as bounded and unitary, without reference to any flow of time during ("I helped him"). Imperfective aspect is used for situations conceived as existing continuously or repetitively as time flows ("I was helping him"; "I used to help people"). Further distinctions can be made, for example, to distinguish states and ongoing actions (continuous and progressive aspects) from repetitive actions (habitual aspect). Certain aspe







Flashcard 1803043605772

Tags
#linguistics #verbs
Question
Imperfective aspect is used for situations conceived as existing [...or...] as time flows
Answer
continuously or repetitively

("I was helping him"; "I used to help people")


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Imperfective aspect is used for situations conceived as existing continuously or repetitively as time flows ("I was helping him"; "I used to help people").

Original toplevel document

Grammatical aspect - Wikipedia
onstruction Singulative-Collective-Pluractive Specificity Subject/Object Suffixaufnahme (Case stacking) Tense Tense–aspect–mood Telicity Transitivity Topic and Comment Thematic relation (Agent/Patient) Valency Voice Volition v t e <span>Aspect is a grammatical category that expresses how an action, event, or state, denoted by a verb, extends over time. Perfective aspect is used in referring to an event conceived as bounded and unitary, without reference to any flow of time during ("I helped him"). Imperfective aspect is used for situations conceived as existing continuously or repetitively as time flows ("I was helping him"; "I used to help people"). Further distinctions can be made, for example, to distinguish states and ongoing actions (continuous and progressive aspects) from repetitive actions (habitual aspect). Certain aspe







Flashcard 1803045965068

Tags
#linguistics #verbs
Question
[...] is used for situations conceived as existing continuously or repetitively as time flows .
Answer
Imperfective aspect

("I was helping him"; "I used to help people")


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Imperfective aspect is used for situations conceived as existing continuously or repetitively as time flows ("I was helping him"; "I used to help people"). </htm

Original toplevel document

Grammatical aspect - Wikipedia
onstruction Singulative-Collective-Pluractive Specificity Subject/Object Suffixaufnahme (Case stacking) Tense Tense–aspect–mood Telicity Transitivity Topic and Comment Thematic relation (Agent/Patient) Valency Voice Volition v t e <span>Aspect is a grammatical category that expresses how an action, event, or state, denoted by a verb, extends over time. Perfective aspect is used in referring to an event conceived as bounded and unitary, without reference to any flow of time during ("I helped him"). Imperfective aspect is used for situations conceived as existing continuously or repetitively as time flows ("I was helping him"; "I used to help people"). Further distinctions can be made, for example, to distinguish states and ongoing actions (continuous and progressive aspects) from repetitive actions (habitual aspect). Certain aspe







Flashcard 1803055926540

[unknown IMAGE 1803053042956]
Tags
#has-images #jaynes #plausible-reasoning
Question
Given
A ≡ it will start to rain by 10 am at the latest;
B ≡ the sky will become cloudy before 10 am.

The second weaker syllogism (A is false) takes the form [...]


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

pdf

cannot see any pdfs







Flashcard 1803058547980

[unknown IMAGE 1803054615820]
Tags
#has-images #jaynes #plausible-reasoning
Question
Given
A ≡ man is a burglar;
B ≡ man behave weird.

The still weaker syllogism takes the form [...]


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

pdf

cannot see any pdfs







#jaynes #plausible-reasoning
we conceal how complicated our daily reasoning process really is by calling it common sense

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#jaynes #plausible-reasoning
in the burglar/police case, our reasoning depends very much on prior information to help us in evaluating the degree of plausibility

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#jaynes #plausible-reasoning
In trying to understand common sense, we make progress by constructing idealized mathematical models which reproduce a few of its features

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#jaynes #plausible-reasoning
Often, the things which are most familiar to us turn out to be the hardest to understand. Phenomena whose very existence is unknown to the vast majority of the human race (such as the differ- ence in ultraviolet spectra of iron and nickel) can be explained in exhaustive mathematical detail – but all of modern science is practically helpless when faced with the complications of such a commonplace fact as growth of a blade of grass

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#jaynes #plausible-reasoning
advance in knowledge often leads to consequences of great practical value, but of an unpredictable nature

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#jaynes #plausible-reasoning
In principle, the only operations which a machine cannot perform for us are those which we cannot describe in detail, or which could not be completed in a finite number of steps.

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#jaynes #plausible-reasoning
a mathematical model reproduces a part of common sense by prescribing a definite set of operations, this shows us how to ‘build a machine’, (i.e. write a computer program) which operates on incomplete information and, by applying quantitative versions of the above weak syllogisms, does plausible reasoning instead of deductive reasoning.

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#jaynes #plausible-reasoning
Our unaided common sense can decide between a few distinctive hypotheses, but not many similar ones.

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




Flashcard 1803073752332

Tags
#jaynes #plausible-reasoning
Question
in the burglar/police case, our reasoning depends very much on [...] to help us in evaluating the degree of plausibility
Answer
prior information


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
in the burglar/police case, our reasoning depends very much on prior information to help us in evaluating the degree of plausibility

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1803075325196

Tags
#jaynes #plausible-reasoning
Question
in the burglar/police case, our reasoning depends very much on prior information to help us in evaluating the [...]
Answer
degree of plausibility


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
in the burglar/police case, our reasoning depends very much on prior information to help us in evaluating the degree of plausibility

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1803078470924

Tags
#jaynes #plausible-reasoning
Question
we conceal how complicated our [...] really is by calling it common sense
Answer
daily reasoning process


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
we conceal how complicated our daily reasoning process really is by calling it common sense

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1803080043788

Tags
#jaynes #plausible-reasoning
Question
we conceal how complicated our daily reasoning process really is by calling it [...]
Answer
common sense


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
we conceal how complicated our daily reasoning process really is by calling it common sense

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1803083189516

Tags
#jaynes #plausible-reasoning
Question
advance in knowledge often leads to consequences of great practical value, but of [...] nature
Answer
an unpredictable


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
advance in knowledge often leads to consequences of great practical value, but of an unpredictable nature

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1803084762380

Tags
#jaynes #plausible-reasoning
Question
advance in knowledge often leads to consequences of [...] value, but of an unpredictable nature
Answer
great practical


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
advance in knowledge often leads to consequences of great practical value, but of an unpredictable nature

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1803086335244

Tags
#jaynes #plausible-reasoning
Question
In principle, the only operations which a machine cannot perform for us are those which [...], or which could not be completed in a finite number of steps.
Answer
we cannot describe in detail


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
In principle, the only operations which a machine cannot perform for us are those which we cannot describe in detail, or which could not be completed in a finite number of steps.

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1803087908108

Tags
#jaynes #plausible-reasoning
Question
In principle, the only operations which a machine cannot perform for us are those which we cannot describe in detail, or which [...].
Answer
could not be completed in a finite number of steps


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
In principle, the only operations which a machine cannot perform for us are those which we cannot describe in detail, or which could not be completed in a finite number of steps.

Original toplevel document (pdf)

cannot see any pdfs







#jaynes #plausible-reasoning
a mathematical model reproduces a part of common sense by prescribing a definite set of operations

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on


Parent (intermediate) annotation

Open it
a mathematical model reproduces a part of common sense by prescribing a definite set of operations, this shows us how to ‘build a machine’, (i.e. write a computer program) which operates on incomplete information and, by applying quantitative versions of the above weak syllogisms, do

Original toplevel document (pdf)

cannot see any pdfs




#jaynes #plausible-reasoning
A model operates on incomplete information and, by applying quantitative versions of the above weak syllogisms, does plausible reasoning instead of deductive reasoning.

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on


Parent (intermediate) annotation

Open it
a mathematical model reproduces a part of common sense by prescribing a definite set of operations, this shows us how to ‘build a machine’, (i.e. write a computer program) which operates on incomplete information and, by applying quantitative versions of the above weak syllogisms, does plausible reasoning instead of deductive reasoning.

Original toplevel document (pdf)

cannot see any pdfs




Flashcard 1803093413132

Tags
#jaynes #plausible-reasoning
Question
[...] reproduces a part of common sense by prescribing a definite set of operations
Answer
a mathematical model


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
a mathematical model reproduces a part of common sense by prescribing a definite set of operations

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1803094985996

Tags
#jaynes #plausible-reasoning
Question
a mathematical model reproduces [...] by prescribing a definite set of operations
Answer
a part of common sense


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
a mathematical model reproduces a part of common sense by prescribing a definite set of operations

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1803096558860

Tags
#jaynes #plausible-reasoning
Question
a mathematical model reproduces a part of common sense by prescribing [...]
Answer
a definite set of operations


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
a mathematical model reproduces a part of common sense by prescribing a definite set of operations

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1803099704588

Tags
#jaynes #plausible-reasoning
Question
A model operates on [...how much...] information
Answer
incomplete


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
A model operates on incomplete information and, by applying quantitative versions of the above weak syllogisms, does plausible reasoning instead of deductive reasoning.

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1803102850316

Tags
#jaynes #plausible-reasoning
Question
A model does [...] instead of deductive reasoning.
Answer
plausible reasoning


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
A model operates on incomplete information and, by applying quantitative versions of the above weak syllogisms, does plausible reasoning instead of deductive reasoning.

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1803105209612

Tags
#jaynes #plausible-reasoning
Question
A model does plausible reasoning by applying [...]
Answer
quantitative versions of the weak syllogisms


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
A model operates on incomplete information and, by applying quantitative versions of the above weak syllogisms, does plausible reasoning instead of deductive reasoning.

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1803108355340

Tags
#jaynes #plausible-reasoning
Question
Our unaided common sense can decide between [...] but not [...]
Answer
a few distinctive hypotheses, many similar ones


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Our unaided common sense can decide between a few distinctive hypotheses, but not many similar ones.

Original toplevel document (pdf)

cannot see any pdfs







smart contract

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

La Blockchain nel Comune di Torino | Il Blog di Beppe Grillo
saustiva panoramica su potenzialità, limiti e situazione attuale della tecnologia, e approfondimenti mirati su utilizzi specifici quali disintermediazione dei processi di autenticazione, autorizzazione e audit, token, transazioni multi-firma, <span>smart contract. Questi ultimi, rappresentano un’enorme risorsa per il futuro, andando a rafforzare o addirittura a sostituire il sistema dei contratti tradizionali, con un abbattimento dei costi, de




smart contract è il fatto che sono eseguiti automaticamente, senza bisogno di intermediari e, allo stesso tempo, proprio per il meccanismo di verifica reciproca dei blocchi, possono consentire anche a soggetti che non si conoscono e non si fidano reciprocamente di interagire e concludere una transazione.

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

La Blockchain nel Comune di Torino | Il Blog di Beppe Grillo
Questi ultimi, rappresentano un’enorme risorsa per il futuro, andando a rafforzare o addirittura a sostituire il sistema dei contratti tradizionali, con un abbattimento dei costi, dei tempi e dei rischi di inadempienza. Punto di forza degli <span>smart contract è il fatto che sono eseguiti automaticamente, senza bisogno di intermediari e, allo stesso tempo, proprio per il meccanismo di verifica reciproca dei blocchi, possono consentire anche a soggetti che non si conoscono e non si fidano reciprocamente di interagire e concludere una transazione. Il secondo giorno si è stato chiesto agli attori di proporre possibili servizi, disposizioni di legge o processi amministrativi (in ambito Pubblica Amministrazione) per le quali l’app




token sociale (riconoscere, tracciare e incentivare l’impegno civile)

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

La Blockchain nel Comune di Torino | Il Blog di Beppe Grillo
12 possibili progetti oggetto di studio e per ognuno di essi sono stati analizzati punti di forza, debolezze, rischi e opportunitá. Fra quelli che hanno ottenuto più consensi, l’utilizzo della blockchain per identità digitale multilivello, <span>token sociale (riconoscere, tracciare e incentivare l’impegno civile), sovranità e riconducibilità del dato, E-procurement (gestione gare, tracciabilità degli acquisti PA, registro fornitori trasparente) e tracciabilità e conservazione delle ricevute tele




E-procurement

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

La Blockchain nel Comune di Torino | Il Blog di Beppe Grillo
, rischi e opportunitá. Fra quelli che hanno ottenuto più consensi, l’utilizzo della blockchain per identità digitale multilivello, token sociale (riconoscere, tracciare e incentivare l’impegno civile), sovranità e riconducibilità del dato, <span>E-procurement (gestione gare, tracciabilità degli acquisti PA, registro fornitori trasparente) e tracciabilità e conservazione delle ricevute telematiche Pago PA. Una delle riflessioni più interess




Prossima tappa: la creazione di un osservatorio sulla blockchain e una call for action sulle aziende.

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

La Blockchain nel Comune di Torino | Il Blog di Beppe Grillo
i innovazione, ossia non solamente mettere in pratica l’utilizzo della tecnologia nella Pubblica Amministrazione, ma anche far sì che il territorio diventi attrattivo e luogo privilegiato per aziende e start-up che utilizzano la blockchain. <span>Prossima tappa: la creazione di un osservatorio sulla blockchain e una call for action sulle aziende. TAGS featured Condividi Facebook Twitter Articolo precedenteLa Nuova Zelanda vieta le trivellazioni offshore Prossimo articoloSensazione di tatto sul braccio d




Il 15 dicembre 2017 al fine di accrescere e divulgare sul territorio la conoscenza della Blockchain, Città di Torino, Università degli Studi di Torino e Nesta Italia, in collaborazione con numerosi altri partner, hanno organizzato “Blockchain for Social Good”, primo evento in Italia sulla blockchain e le sue applicazioni in ambito non finanziario, a cui hanno partecipato relatori nazionali e internazionali provenienti da pubblica amministrazione, mondo universitario, imprese private, no-profit ed enti di ricerca.

Nell’occasione è stato lanciato un premio di 5 milioni di euro promosso dalla Commissione Europea, un concorso aperto a privati, enti giuridici e organizzazioni internazionali per sviluppare soluzioni innovative, efficienti e ad alto impatto sociale utilizzando la tecnologia della blockchain.

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

La Blockchain nel Comune di Torino | Il Blog di Beppe Grillo
condono uno o più informatici, pubblicò il protocollo Bitcoin. Nata come infrastruttura per gli scambi in criptovalute, solo in un secondo momento il suo utilizzo è stato allargato come impianto su cui eseguire altri tipi di applicazione. <span>Il 15 dicembre 2017 al fine di accrescere e divulgare sul territorio la conoscenza della Blockchain, Città di Torino, Università degli Studi di Torino e Nesta Italia, in collaborazione con numerosi altri partner, hanno organizzato “Blockchain for Social Good”, primo evento in Italia sulla blockchain e le sue applicazioni in ambito non finanziario, a cui hanno partecipato relatori nazionali e internazionali provenienti da pubblica amministrazione, mondo universitario, imprese private, no-profit ed enti di ricerca. Nell’occasione è stato lanciato un premio di 5 milioni di euro promosso dalla Commissione Europea, un concorso aperto a privati, enti giuridici e organizzazioni internazionali per sviluppare soluzioni innovative, efficienti e ad alto impatto sociale utilizzando la tecnologia della blockchain. I primi progetti in cui è stata avviata un’applicazione sperimentale di blockchain nella nostra città sono CRIO (Strumenti per la Lotta al Cyberbullismo sui social network ​nell’ambit