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#### Annotation 1729484950796

 #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.

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

#### Annotation 1729497271564

 #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 .

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

#### Annotation 1729702268172

 #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.

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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)

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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

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The negative binomial distribution also arises as a [...] of Poisson distributions
continuous mixture

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

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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.

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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

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[...] also arises as a continuous mixture as Gamma-Poisson distributions
The negative binomial distribution

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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

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

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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

#### Annotation 1731647638796

 #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.

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

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a [...] form of a mathematical object is a standard way of presenting that object as a mathematical expression.
canonical, normal, or standard form

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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.

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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

#### Annotation 1737367620876

 #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 matrixAn $$n \times m$$ matrix C called the volatility matrixA $$k \times n$$ 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} . .

#### Annotation 1737373125900

 #linear-state-space-models A martingale difference sequence is a sequence that is zero mean when conditioned on past information

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

#### Annotation 1737374698764

 #linear-state-space-models 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 )

#### Annotation 1737384922380

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

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

#### Annotation 1737388592396

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

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

#### Annotation 1737390689548

 #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

#### Annotation 1737393048844

 #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.

#### Annotation 1737395145996

 #linear-state-space-models However, global stability is more than we need for stationary solutions, and often more than we want

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

#### Annotation 1737397243148

 #linear-state-space-models Ergodicity is the property that time series and ensemble averages coincide

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(

#### Annotation 1737399340300

 #linear-state-space-models More formally, ergodicity implies that time series sample averages converge to their expectation under the stationary distribution

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

#### Annotation 1737401437452

 #linear-state-space-models In our linear Gaussian setting, any covariance stationary process is also ergodic

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

#### Annotation 1737403534604

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

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

#### Annotation 1737405893900

 #linear-state-space-models The natural way to predict variables is to use conditional distributions

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

#### Annotation 1737408253196

 #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

#### Flashcard 1737413758220

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In state space models, finding [...] is an art
the state

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In state space models, finding the state is an art

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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

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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
a stream of dividends

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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

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Linear State Space Models – Quantitative Economics

#### Flashcard 1737421098252

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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

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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

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Linear State Space Models – Quantitative Economics

#### Flashcard 1737422671116

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

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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

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Linear State Space Models – Quantitative Economics

#### Flashcard 1737424243980

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The natural way to predict variables is to use

[...]

conditional distributions

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The natural way to predict variables is to use conditional distributions

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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

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

ergodic

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

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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

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

covariance stationary

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

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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

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

our linear Gaussian

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

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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

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

time series sample averages

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

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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

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

[...]

their expectation under the stationary distribution

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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

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

time series and ensemble

The ensemble is the samples from the stationary distribution

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Ergodicity is the property that time series and ensemble averages coincide

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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

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

global stability

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

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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

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

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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

#### Original toplevel document

Linear State Space Models – Quantitative Economics

#### Flashcard 1737442594060

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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

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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

#### Original toplevel document

Linear State Space Models – Quantitative Economics

#### Flashcard 1737444166924

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[...] form a list of objects that characterize a population distribution
Sufficient statistics

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

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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

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#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 [...]

volatility matrix

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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) . .

#### Original toplevel document

Linear State Space Models – Quantitative Economics

#### Flashcard 1737453341964

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#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 [...].
all eigenvalues of A have moduli strictly less than unity

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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.

#### Original toplevel document

Linear State Space Models – Quantitative Economics

#### Flashcard 1737454914828

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the [...] distribution is specialized to $$N(0,I)$$
shock

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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 )

#### Original toplevel document

Linear State Space Models – Quantitative Economics

#### Flashcard 1767820102924

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

prediction

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

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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

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#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 [...]
[unknown IMAGE 1799065308428]

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#### Flashcard 1802947398924

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

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

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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

#### Annotation 1802951068940

 语料库的地址是： http：// corpus.byu.edu/coca/

Unknown title

#### Annotation 1802999303436

 #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").

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

#### Annotation 1803017391372

 #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.).

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

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#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.).
verbal inflections

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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

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

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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

#### Annotation 1803027877132

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

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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

#### Annotation 1803029449996

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

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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

#### Annotation 1803031022860

 #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").

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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

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

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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

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

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Aspect expresses how an action, event extends over time.

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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

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

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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

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

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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

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

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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

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

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

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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

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

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

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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

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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

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 [...]
[unknown IMAGE 1803053042956]

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#### Flashcard 1803058547980

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#has-images #jaynes #plausible-reasoning
Question
Given
A ≡ man is a burglar;
B ≡ man behave weird.

The still weaker syllogism takes the form [...]
[unknown IMAGE 1803054615820]

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#### Annotation 1803060382988

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

#### pdf

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#### Annotation 1803061955852

 #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

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#### Annotation 1803063528716

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

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#### Annotation 1803065101580

 #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

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#### Annotation 1803066674444

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

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#### Annotation 1803068247308

 #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.

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#### Annotation 1803069820172

 #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.

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#### Annotation 1803071393036

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

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#### Flashcard 1803073752332

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

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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)

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#### Flashcard 1803075325196

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

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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)

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#### Flashcard 1803078470924

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

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

#### Original toplevel document (pdf)

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#### Flashcard 1803080043788

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

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

#### Original toplevel document (pdf)

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#### Flashcard 1803083189516

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

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

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#### Flashcard 1803084762380

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

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

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#### Flashcard 1803086335244

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#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.
we cannot describe in detail

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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)

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#### 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 [...].
could not be completed in a finite number of steps

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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.

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#### Annotation 1803090267404

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

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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

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#### Annotation 1803091840268

 #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.

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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.

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#### Flashcard 1803093413132

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

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

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#### Flashcard 1803094985996

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

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

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#### Flashcard 1803096558860

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a mathematical model reproduces a part of common sense by prescribing [...]
a definite set of operations

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

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

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

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#### Flashcard 1803102850316

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

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

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#### Flashcard 1803105209612

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

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

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#### Flashcard 1803108355340

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

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

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#### Annotation 1803113598220

 smart contract

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

#### Annotation 1803115695372

 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.

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

#### Annotation 1803117268236

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

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

#### Annotation 1803118841100

 E-procurement

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

#### Annotation 1803120413964

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

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

#### Annotation 1803121986828

 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.