Edited, memorised or added to reading list

on 27-Jan-2018 (Sat)

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

adding article
you MUST have a user

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

Wikipedia:FAQ - Wikipedia, the free encyclopedia
cal, legal, financial, safety, and other critical issues? 8 Who owns Wikipedia? 9 Why am I having trouble logging in? 10 How can I contact Wikipedia? How do I create a new page? <span>You are required to have a Wikipedia account to create a new article—you can register here. To see other benefits to creating an account, see Why create an account? For creating a new article see Wikipedia:Your first article and Wikipedia:Article development; and you may wi




Flashcard 1479826083084

Tags
#cfa-level-1 #reading-25-understanding-income-statement #revenue-recognition
Question
Under the completed contract method, what does it mean for the contract to be substantially finished?
Answer
That the remaining costs and potential risks are insignificant in amount


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
Under the completed contract method, the company does not report any income until the contract is substantially finished (the remaining costs and potential risks are insignificant in amount), although provision should be made for expected losses.

Original toplevel document

3.2.1. Long-Term Contracts
d incurred. Under this method, no profit is recognized until all the costs had been recovered. Under US GAAP, but not under IFRS, a revenue recognition method used when the outcome cannot be measured reliably is the completed contract method. <span>Under the completed contract method, the company does not report any income until the contract is substantially finished (the remaining costs and potential risks are insignificant in amount), although provision should be made for expected losses. Billings and costs are accumulated on the balance sheet rather than flowing through the income statement. Under US GAAP, the completed contract method is also acceptable when the entity







Flashcard 1644508089612

Question

5-2 = [...] = [...]

Answer
1/(52) = 1/25


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






#spanish
Él seguía deprimido, aunque dijo que había dormido bien.

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




#metric-space
In mathematics, the Banach fixed-point theorem (also known as the contraction mapping theorem) is an important tool in the theory of metric spaces; it guarantees the existence and uniqueness of fixed points of certain self-maps of metric spaces, and provides a constructive method to find those fixed points.

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

Banach fixed-point theorem - Wikipedia
Banach fixed-point theorem - Wikipedia Banach fixed-point theorem From Wikipedia, the free encyclopedia Jump to: navigation, search In mathematics, the Banach fixed-point theorem (also known as the contraction mapping theorem or contraction mapping principle) is an important tool in the theory of metric spaces; it guarantees the existence and uniqueness of fixed points of certain self-maps of metric spaces, and provides a constructive method to find those fixed points. The theorem is named after Stefan Banach (1892–1945), and was first stated by him in 1922. [1] Contents [hide] 1 Statement 2 Proofs 2.1 Banach's original proof 2.2 Shorter




Flashcard 1738846375180

Tags
#metric-space
Question
the Banach fixed-point theorem guarantees [...] of certain self-maps of metric spaces, and provides a constructive method to find those fixed points.
Answer
the existence and uniqueness of fixed points


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
d><head> In mathematics, the Banach fixed-point theorem (also known as the contraction mapping theorem or contraction mapping principle) is an important tool in the theory of metric spaces; it guarantees the existence and uniqueness of fixed points of certain self-maps of metric spaces, and provides a constructive method to find those fixed points. <html>

Original toplevel document

Banach fixed-point theorem - Wikipedia
Banach fixed-point theorem - Wikipedia Banach fixed-point theorem From Wikipedia, the free encyclopedia Jump to: navigation, search In mathematics, the Banach fixed-point theorem (also known as the contraction mapping theorem or contraction mapping principle) is an important tool in the theory of metric spaces; it guarantees the existence and uniqueness of fixed points of certain self-maps of metric spaces, and provides a constructive method to find those fixed points. The theorem is named after Stefan Banach (1892–1945), and was first stated by him in 1922. [1] Contents [hide] 1 Statement 2 Proofs 2.1 Banach's original proof 2.2 Shorter







Flashcard 1738856074508

Tags
#metric-space
Question
the Banach fixed-point theorem is also known as [...]
Answer
the contraction mapping theorem


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, the Banach fixed-point theorem (also known as the contraction mapping theorem) is an important tool in the theory of metric spaces; it guarantees the existence and uniqueness of fixed points of certain self-maps of metric spaces, and provides a constructive metho

Original toplevel document

Banach fixed-point theorem - Wikipedia
Banach fixed-point theorem - Wikipedia Banach fixed-point theorem From Wikipedia, the free encyclopedia Jump to: navigation, search In mathematics, the Banach fixed-point theorem (also known as the contraction mapping theorem or contraction mapping principle) is an important tool in the theory of metric spaces; it guarantees the existence and uniqueness of fixed points of certain self-maps of metric spaces, and provides a constructive method to find those fixed points. The theorem is named after Stefan Banach (1892–1945), and was first stated by him in 1922. [1] Contents [hide] 1 Statement 2 Proofs 2.1 Banach's original proof 2.2 Shorter







#metric-space

Definition. Let (X, d) be a metric space. Then a map T : XX is called a contraction mapping on X if there exists q ∈ [0, 1) such that for all x, y in X.

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

Banach fixed-point theorem - Wikipedia
was first stated by him in 1922. [1] Contents [hide] 1 Statement 2 Proofs 2.1 Banach's original proof 2.2 Shorter proof 3 Applications 4 Converses 5 Generalizations 6 See also 7 Notes 8 References Statement[edit source] <span>Definition. Let (X, d) be a metric space. Then a map T : X → X is called a contraction mapping on X if there exists q ∈ [0, 1) such that d ( T ( x ) , T ( y ) ) ≤ q d ( x , y ) {\displaystyle d(T(x),T(y))\leq qd(x,y)} for all x, y in X. Banach Fixed Point Theorem. Let (X, d) be a non-empty complete metric space with a contraction mapping T : X → X. Then T admits a unique fixed-point x* in X (i.e. T(x*) = x*). Furth




#metric-space
Banach Fixed Point Theorem. Let (X, d) be a non-empty complete metric space with a contraction mapping T : XX. Then T admits a unique fixed-point x* in X (i.e. T(x*) = x*). Furthermore, x* can be found as follows: start with an arbitrary element x0 in X and define a sequence {xn} by xn = T(xn−1), then xnx* .

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

Banach fixed-point theorem - Wikipedia
x ) , T ( y ) ) ≤ q d ( x , y ) {\displaystyle d(T(x),T(y))\leq qd(x,y)} for all x, y in X. <span>Banach Fixed Point Theorem. Let (X, d) be a non-empty complete metric space with a contraction mapping T : X → X. Then T admits a unique fixed-point x* in X (i.e. T(x*) = x*). Furthermore, x* can be found as follows: start with an arbitrary element x 0 in X and define a sequence {x n } by x n = T(x n−1 ), then x n → x*. Remark 1. The following inequalities are equivalent and describe the speed of convergence: d




Flashcard 1738862628108

Tags
#metric-space
Question

Definition. Let (X, d) be a metric space. Then a map T : XX is called a [...] on X if there exists q ∈ [0, 1) such that for all x, y in X.


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
Definition . Let (X, d) be a metric space. Then a map T : X → X is called a contraction mapping on X if there exists q ∈ [0, 1) such that for all x, y in X.

Original toplevel document

Banach fixed-point theorem - Wikipedia
was first stated by him in 1922. [1] Contents [hide] 1 Statement 2 Proofs 2.1 Banach's original proof 2.2 Shorter proof 3 Applications 4 Converses 5 Generalizations 6 See also 7 Notes 8 References Statement[edit source] <span>Definition. Let (X, d) be a metric space. Then a map T : X → X is called a contraction mapping on X if there exists q ∈ [0, 1) such that d ( T ( x ) , T ( y ) ) ≤ q d ( x , y ) {\displaystyle d(T(x),T(y))\leq qd(x,y)} for all x, y in X. Banach Fixed Point Theorem. Let (X, d) be a non-empty complete metric space with a contraction mapping T : X → X. Then T admits a unique fixed-point x* in X (i.e. T(x*) = x*). Furth







Flashcard 1738864200972

Tags
#metric-space
Question

Definition. Let (X, d) be a metric space. Then a map T : XX is called a contraction mapping on X if there exists q ∈ [0, 1) such that [...] for all x, y in X.

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
Definition . Let (X, d) be a metric space. Then a map T : X → X is called a contraction mapping on X if there exists q ∈ [0, 1) such that for all x, y in X.

Original toplevel document

Banach fixed-point theorem - Wikipedia
was first stated by him in 1922. [1] Contents [hide] 1 Statement 2 Proofs 2.1 Banach's original proof 2.2 Shorter proof 3 Applications 4 Converses 5 Generalizations 6 See also 7 Notes 8 References Statement[edit source] <span>Definition. Let (X, d) be a metric space. Then a map T : X → X is called a contraction mapping on X if there exists q ∈ [0, 1) such that d ( T ( x ) , T ( y ) ) ≤ q d ( x , y ) {\displaystyle d(T(x),T(y))\leq qd(x,y)} for all x, y in X. Banach Fixed Point Theorem. Let (X, d) be a non-empty complete metric space with a contraction mapping T : X → X. Then T admits a unique fixed-point x* in X (i.e. T(x*) = x*). Furth







Flashcard 1738867084556

Tags
#metric-space
Question
Let (X, d) be a non-empty complete metric space with a contraction mapping T : XX. Then T admits [...]
Answer
a unique fixed-point x* in X


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
Banach Fixed Point Theorem. Let (X, d) be a non-empty complete metric space with a contraction mapping T : X → X. Then T admits a unique fixed-point x* in X (i.e. T(x*) = x*). Furthermore, x* can be found as follows: start with an arbitrary element x 0 in X and define a sequence {x n } by x n = T(x n−1 ), then x n → x* . <

Original toplevel document

Banach fixed-point theorem - Wikipedia
x ) , T ( y ) ) ≤ q d ( x , y ) {\displaystyle d(T(x),T(y))\leq qd(x,y)} for all x, y in X. <span>Banach Fixed Point Theorem. Let (X, d) be a non-empty complete metric space with a contraction mapping T : X → X. Then T admits a unique fixed-point x* in X (i.e. T(x*) = x*). Furthermore, x* can be found as follows: start with an arbitrary element x 0 in X and define a sequence {x n } by x n = T(x n−1 ), then x n → x*. Remark 1. The following inequalities are equivalent and describe the speed of convergence: d







Flashcard 1738868919564

Tags
#metric-space
Question
When a complete metric space admits a contraction mapping T : XX. The fixed point for the map can be found as follows: start with [...] and define a sequence {xn} by xn = T(xn−1), then xnx* .
Answer
an arbitrary element x0 in X


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
Banach Fixed Point Theorem. Let (X, d) be a non-empty complete metric space with a contraction mapping T : X → X. Then T admits a unique fixed-point x* in X (i.e. T(x*) = x*). Furthermore, x* can be found as follows: start with <span>an arbitrary element x 0 in X and define a sequence {x n } by x n = T(x n−1 ), then x n → x* . <span><body><html>

Original toplevel document

Banach fixed-point theorem - Wikipedia
x ) , T ( y ) ) ≤ q d ( x , y ) {\displaystyle d(T(x),T(y))\leq qd(x,y)} for all x, y in X. <span>Banach Fixed Point Theorem. Let (X, d) be a non-empty complete metric space with a contraction mapping T : X → X. Then T admits a unique fixed-point x* in X (i.e. T(x*) = x*). Furthermore, x* can be found as follows: start with an arbitrary element x 0 in X and define a sequence {x n } by x n = T(x n−1 ), then x n → x*. Remark 1. The following inequalities are equivalent and describe the speed of convergence: d







Flashcard 1738870492428

Tags
#metric-space
Question
Using the Banach Fixed Point Theorem the fixed point x* can be found by starting with an arbitrary element x0 in X and define a sequence {xn} by [...], then xnx* .
Answer
xn = T(xn−1)


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
empty complete metric space with a contraction mapping T : X → X. Then T admits a unique fixed-point x* in X (i.e. T(x*) = x*). Furthermore, x* can be found as follows: start with an arbitrary element x 0 in X and define a sequence {x n } by <span>x n = T(x n−1 ), then x n → x* . <span><body><html>

Original toplevel document

Banach fixed-point theorem - Wikipedia
x ) , T ( y ) ) ≤ q d ( x , y ) {\displaystyle d(T(x),T(y))\leq qd(x,y)} for all x, y in X. <span>Banach Fixed Point Theorem. Let (X, d) be a non-empty complete metric space with a contraction mapping T : X → X. Then T admits a unique fixed-point x* in X (i.e. T(x*) = x*). Furthermore, x* can be found as follows: start with an arbitrary element x 0 in X and define a sequence {x n } by x n = T(x n−1 ), then x n → x*. Remark 1. The following inequalities are equivalent and describe the speed of convergence: d







#graphical-models
A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables.

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

Graphical model - Wikipedia
list of references, but its sources remain unclear because it has insufficient inline citations. Please help to improve this article by introducing more precise citations. (May 2017) (Learn how and when to remove this template message) <span>A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables. They are commonly used in probability theory, statistics—particularly Bayesian statistics—and machine learning. [imagelink] An example of a graphical model. Each arrow indicates




#graphical-models

In a Bayesian network, the network structure of the model is a directed acyclic graph, the model represents a factorization of the joint probability of all random variables. More precisely, if the events are then the joint probability satisfies

where is the set of parents of node . In other words, the joint distribution factors into a product of conditional distributions.

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

Graphical model - Wikipedia
the properties of factorization and independences, but they differ in the set of independences they can encode and the factorization of the distribution that they induce. [1] Bayesian network[edit source] Main article: Bayesian network <span>If the network structure of the model is a directed acyclic graph, the model represents a factorization of the joint probability of all random variables. More precisely, if the events are X 1 , … , X n {\displaystyle X_{1},\ldots ,X_{n}} then the joint probability satisfies P [ X 1 , … , X n ] = ∏ i = 1 n P [ X i | p a i ] {\displaystyle P[X_{1},\ldots ,X_{n}]=\prod _{i=1}^{n}P[X_{i}|pa_{i}]} where p a i {\displaystyle pa_{i}} is the set of parents of node X i {\displaystyle X_{i}} . In other words, the joint distribution factors into a product of conditional distributions. For example, the graphical model in the Figure shown above (which is actually not a directed acyclic graph, but an ancestral graph) consists of the random variables




#bayesian-network
Formally, Bayesian networks are DAGs whose:
  1. nodes represent variables in the Bayesian sense: they may be observable quantities, latent variables, unknown parameters or hypotheses.
  2. Edges represent conditional dependencies;
  3. nodes that are not connected (there is no path from one of the variables to the other in the Bayesian network) represent variables that are conditionally independent of each other.
  4. Each node is associated with a probability function that takes, as input, a particular set of values for the node's parent variables, and gives (as output) the probability (or probability distribution, if applicable) of the variable represented by the node.

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

Bayesian network - Wikipedia
ed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. <span>Formally, Bayesian networks are DAGs whose nodes represent variables in the Bayesian sense: they may be observable quantities, latent variables, unknown parameters or hypotheses. Edges represent conditional dependencies; nodes that are not connected (there is no path from one of the variables to the other in the Bayesian network) represent variables that are conditionally independent of each other. Each node is associated with a probability function that takes, as input, a particular set of values for the node's parent variables, and gives (as output) the probability (or probability distribution, if applicable) of the variable represented by the node. For example, if m {\displaystyle m} parent nodes represent m {\displaystyle m} Boolean variables




#graphical-models
A Markov random field, also known as a Markov network, is a model over an undirected graph.

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

Graphical model - Wikipedia
ne learning models like hidden Markov models, neural networks and newer models such as variable-order Markov models can be considered special cases of Bayesian networks. Markov random field[edit source] Main article: Markov random field <span>A Markov random field, also known as a Markov network, is a model over an undirected graph. A graphical model with many repeated subunits can be represented with plate notation. Other types[edit source] A factor graph is an undirected bipartite graph connecting variables a




Flashcard 1739036953868

Tags
#graphical-models
Question
In a graphical model, a graph expresses [...] between random variables.
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
A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables.

Original toplevel document

Graphical model - Wikipedia
list of references, but its sources remain unclear because it has insufficient inline citations. Please help to improve this article by introducing more precise citations. (May 2017) (Learn how and when to remove this template message) <span>A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables. They are commonly used in probability theory, statistics—particularly Bayesian statistics—and machine learning. [imagelink] An example of a graphical model. Each arrow indicates







Flashcard 1739039313164

Tags
#graphical-models
Question

In a Bayesian network, the network structure of the model is a [...]


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 a Bayesian network, the network structure of the model is a directed acyclic graph, the model represents a factorization of the joint probability of all random variables. More precisely, if the events are then the joint probability satisfies where is

Original toplevel document

Graphical model - Wikipedia
the properties of factorization and independences, but they differ in the set of independences they can encode and the factorization of the distribution that they induce. [1] Bayesian network[edit source] Main article: Bayesian network <span>If the network structure of the model is a directed acyclic graph, the model represents a factorization of the joint probability of all random variables. More precisely, if the events are X 1 , … , X n {\displaystyle X_{1},\ldots ,X_{n}} then the joint probability satisfies P [ X 1 , … , X n ] = ∏ i = 1 n P [ X i | p a i ] {\displaystyle P[X_{1},\ldots ,X_{n}]=\prod _{i=1}^{n}P[X_{i}|pa_{i}]} where p a i {\displaystyle pa_{i}} is the set of parents of node X i {\displaystyle X_{i}} . In other words, the joint distribution factors into a product of conditional distributions. For example, the graphical model in the Figure shown above (which is actually not a directed acyclic graph, but an ancestral graph) consists of the random variables







Flashcard 1739041934604

Tags
#graphical-models
Question

if the events are the joint probability of a Bayesian network satisfies[...]

Answer

is the set of parents of node .


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 a Bayesian network, the network structure of the model is a directed acyclic graph, the model represents a factorization of the joint probability of all random variables. More precisely, if the events are then the joint probability <span>satisfies where is the set of parents of node . In other words, the joint distribution factors into a product of conditional distributions. <span><body><html>

Original toplevel document

Graphical model - Wikipedia
the properties of factorization and independences, but they differ in the set of independences they can encode and the factorization of the distribution that they induce. [1] Bayesian network[edit source] Main article: Bayesian network <span>If the network structure of the model is a directed acyclic graph, the model represents a factorization of the joint probability of all random variables. More precisely, if the events are X 1 , … , X n {\displaystyle X_{1},\ldots ,X_{n}} then the joint probability satisfies P [ X 1 , … , X n ] = ∏ i = 1 n P [ X i | p a i ] {\displaystyle P[X_{1},\ldots ,X_{n}]=\prod _{i=1}^{n}P[X_{i}|pa_{i}]} where p a i {\displaystyle pa_{i}} is the set of parents of node X i {\displaystyle X_{i}} . In other words, the joint distribution factors into a product of conditional distributions. For example, the graphical model in the Figure shown above (which is actually not a directed acyclic graph, but an ancestral graph) consists of the random variables







Flashcard 1739044556044

Tags
#graphical-models
Question

In a Bayesian network, the joint distribution factors into [...].

Answer
a product of 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
factorization of the joint probability of all random variables. More precisely, if the events are then the joint probability satisfies where is the set of parents of node . In other words, the joint distribution factors into <span>a product of conditional distributions. <span><body><html>

Original toplevel document

Graphical model - Wikipedia
the properties of factorization and independences, but they differ in the set of independences they can encode and the factorization of the distribution that they induce. [1] Bayesian network[edit source] Main article: Bayesian network <span>If the network structure of the model is a directed acyclic graph, the model represents a factorization of the joint probability of all random variables. More precisely, if the events are X 1 , … , X n {\displaystyle X_{1},\ldots ,X_{n}} then the joint probability satisfies P [ X 1 , … , X n ] = ∏ i = 1 n P [ X i | p a i ] {\displaystyle P[X_{1},\ldots ,X_{n}]=\prod _{i=1}^{n}P[X_{i}|pa_{i}]} where p a i {\displaystyle pa_{i}} is the set of parents of node X i {\displaystyle X_{i}} . In other words, the joint distribution factors into a product of conditional distributions. For example, the graphical model in the Figure shown above (which is actually not a directed acyclic graph, but an ancestral graph) consists of the random variables







Flashcard 1739046391052

Tags
#graphical-models
Question
A Markov random field is a model over [...].
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
A Markov random field, also known as a Markov network, is a model over an undirected graph.

Original toplevel document

Graphical model - Wikipedia
ne learning models like hidden Markov models, neural networks and newer models such as variable-order Markov models can be considered special cases of Bayesian networks. Markov random field[edit source] Main article: Markov random field <span>A Markov random field, also known as a Markov network, is a model over an undirected graph. A graphical model with many repeated subunits can be represented with plate notation. Other types[edit source] A factor graph is an undirected bipartite graph connecting variables a







Flashcard 1739048488204

Tags
#graphical-models
Question
A [...] is a model over an undirected graph.
Answer
Markov random field


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 Markov random field, also known as a Markov network, is a model over an undirected graph.

Original toplevel document

Graphical model - Wikipedia
ne learning models like hidden Markov models, neural networks and newer models such as variable-order Markov models can be considered special cases of Bayesian networks. Markov random field[edit source] Main article: Markov random field <span>A Markov random field, also known as a Markov network, is a model over an undirected graph. A graphical model with many repeated subunits can be represented with plate notation. Other types[edit source] A factor graph is an undirected bipartite graph connecting variables a







Flashcard 1739051896076

Tags
#bayesian-network
Question
In Bayesian network graphs [...] represent variables
Answer
nodes


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
Formally, Bayesian networks are DAGs whose: nodes represent variables in the Bayesian sense: they may be observable quantities, latent variables, unknown parameters or hypotheses. Edges represent conditional dependencies; nodes that ar

Original toplevel document

Bayesian network - Wikipedia
ed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. <span>Formally, Bayesian networks are DAGs whose nodes represent variables in the Bayesian sense: they may be observable quantities, latent variables, unknown parameters or hypotheses. Edges represent conditional dependencies; nodes that are not connected (there is no path from one of the variables to the other in the Bayesian network) represent variables that are conditionally independent of each other. Each node is associated with a probability function that takes, as input, a particular set of values for the node's parent variables, and gives (as output) the probability (or probability distribution, if applicable) of the variable represented by the node. For example, if m {\displaystyle m} parent nodes represent m {\displaystyle m} Boolean variables







Flashcard 1739053468940

Tags
#bayesian-network
Question
Graphically, Bayesian networks use [...] to represent conditional dependencies
Answer
Edges


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
html> Formally, Bayesian networks are DAGs whose: nodes represent variables in the Bayesian sense: they may be observable quantities, latent variables, unknown parameters or hypotheses. Edges represent conditional dependencies; nodes that are not connected (there is no path from one of the variables to the other in the Bayesian network) represent variables that are condition

Original toplevel document

Bayesian network - Wikipedia
ed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. <span>Formally, Bayesian networks are DAGs whose nodes represent variables in the Bayesian sense: they may be observable quantities, latent variables, unknown parameters or hypotheses. Edges represent conditional dependencies; nodes that are not connected (there is no path from one of the variables to the other in the Bayesian network) represent variables that are conditionally independent of each other. Each node is associated with a probability function that takes, as input, a particular set of values for the node's parent variables, and gives (as output) the probability (or probability distribution, if applicable) of the variable represented by the node. For example, if m {\displaystyle m} parent nodes represent m {\displaystyle m} Boolean variables







Flashcard 1739055041804

Tags
#bayesian-network
Question
nodes that are not connected represent variables that are [...].
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
ntities, latent variables, unknown parameters or hypotheses. Edges represent conditional dependencies; nodes that are not connected (there is no path from one of the variables to the other in the Bayesian network) represent variables that are <span>conditionally independent of each other. Each node is associated with a probability function that takes, as input, a particular set of values for the node's parent variables, and gives (as output) the probability (or probabil

Original toplevel document

Bayesian network - Wikipedia
ed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. <span>Formally, Bayesian networks are DAGs whose nodes represent variables in the Bayesian sense: they may be observable quantities, latent variables, unknown parameters or hypotheses. Edges represent conditional dependencies; nodes that are not connected (there is no path from one of the variables to the other in the Bayesian network) represent variables that are conditionally independent of each other. Each node is associated with a probability function that takes, as input, a particular set of values for the node's parent variables, and gives (as output) the probability (or probability distribution, if applicable) of the variable represented by the node. For example, if m {\displaystyle m} parent nodes represent m {\displaystyle m} Boolean variables







Flashcard 1739056614668

Tags
#bayesian-network
Question
In a Bayesian network each node takes [...] as input
Answer
a set of values from parent nodes


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
re not connected (there is no path from one of the variables to the other in the Bayesian network) represent variables that are conditionally independent of each other. Each node is associated with a probability function that takes, as input, <span>a particular set of values for the node's parent variables, and gives (as output) the probability (or probability distribution, if applicable) of the variable represented by the node. <span><body><html>

Original toplevel document

Bayesian network - Wikipedia
ed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. <span>Formally, Bayesian networks are DAGs whose nodes represent variables in the Bayesian sense: they may be observable quantities, latent variables, unknown parameters or hypotheses. Edges represent conditional dependencies; nodes that are not connected (there is no path from one of the variables to the other in the Bayesian network) represent variables that are conditionally independent of each other. Each node is associated with a probability function that takes, as input, a particular set of values for the node's parent variables, and gives (as output) the probability (or probability distribution, if applicable) of the variable represented by the node. For example, if m {\displaystyle m} parent nodes represent m {\displaystyle m} Boolean variables







Flashcard 1739058187532

Tags
#bayesian-network
Question
In Bayesian networks each node represents a variable with [...]
Answer
a probability 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
ayesian network) represent variables that are conditionally independent of each other. Each node is associated with a probability function that takes, as input, a particular set of values for the node's parent variables, and gives (as output) <span>the probability (or probability distribution, if applicable) of the variable represented by the node. <span><body><html>

Original toplevel document

Bayesian network - Wikipedia
ed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. <span>Formally, Bayesian networks are DAGs whose nodes represent variables in the Bayesian sense: they may be observable quantities, latent variables, unknown parameters or hypotheses. Edges represent conditional dependencies; nodes that are not connected (there is no path from one of the variables to the other in the Bayesian network) represent variables that are conditionally independent of each other. Each node is associated with a probability function that takes, as input, a particular set of values for the node's parent variables, and gives (as output) the probability (or probability distribution, if applicable) of the variable represented by the node. For example, if m {\displaystyle m} parent nodes represent m {\displaystyle m} Boolean variables







#d-separation
d-separation is a criterion for deciding, from a given a causal graph, whether a set X of variables is independent of another set Y, given a third set Z.

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

Unknown title
CAUSALITY - Discussion d-SEPARATION WITHOUT TEARS (At the request of many readers) Introduction d-separation is a criterion for deciding, from a given a causal graph, whether a set X of variables is independent of another set Y, given a third set Z. The idea is to associate "dependence" with "connectedness" (i.e., the existence of a connecting path) and "independence" with "unconnected-ness"




#d-separation
The idea (of d-separation) is to associate "dependence" with "connectedness" (i.e., the existence of a connecting path) and "independence" with "unconnected-ness" or "separation".
The only twist on this simple idea is to define what we mean by "connecting path", given that
  1. we are dealing with a system of directed arrows
  2. in the graph some vertices correspond to measured variables, whose values are known precisely.
To account for the orientations of the arrows we use the terms "d-separated" and "d-connected" (d connotes "directional").

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

Unknown title
d-SEPARATION WITHOUT TEARS (At the request of many readers) Introduction d-separation is a criterion for deciding, from a given a causal graph, whether a set X of variables is independent of another set Y, given a third set Z. <span>The idea is to associate "dependence" with "connectedness" (i.e., the existence of a connecting path) and "independence" with "unconnected-ness" or "separation". The only twist on this simple idea is to define what we mean by "connecting path", given that we are dealing with a system of directed arrows in which some vertices (those residing in Z) correspond to measured variables, whose values are known precisely. To account for the orientations of the arrows we use the terms "d-separated" and "d-connected" (d connotes "directional"). We start by considering separation between two singleton variables, x and y; the extension to sets of variables is straightforward (i.e., two sets are separated if and only if each el




Flashcard 1739068411148

Tags
#d-separation
Question
The idea of d-separation is complicated by defining exactly what is a [...], given that the edges are directed and some variables are already measured.
Answer
connecting path


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
nce" with "connectedness" (i.e., the existence of a connecting path) and "independence" with "unconnected-ness" or "separation". The only twist on this simple idea is to define what we mean by <span>"connecting path", given that we are dealing with a system of directed arrows in which some vertices (those residing in Z) correspond to measured variables, whose values are known precisely. To account f

Original toplevel document

Unknown title
d-SEPARATION WITHOUT TEARS (At the request of many readers) Introduction d-separation is a criterion for deciding, from a given a causal graph, whether a set X of variables is independent of another set Y, given a third set Z. <span>The idea is to associate "dependence" with "connectedness" (i.e., the existence of a connecting path) and "independence" with "unconnected-ness" or "separation". The only twist on this simple idea is to define what we mean by "connecting path", given that we are dealing with a system of directed arrows in which some vertices (those residing in Z) correspond to measured variables, whose values are known precisely. To account for the orientations of the arrows we use the terms "d-separated" and "d-connected" (d connotes "directional"). We start by considering separation between two singleton variables, x and y; the extension to sets of variables is straightforward (i.e., two sets are separated if and only if each el







Flashcard 1739069984012

Tags
#d-separation
Question
The only twist on this simple idea of "connecting path" is that we are dealing with a system of directed arrows in which some vertices correspond to [...], whose values are known precisely.
Answer
measured variables


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
or "separation". The only twist on this simple idea is to define what we mean by "connecting path", given that we are dealing with a system of directed arrows in which some vertices (those residing in Z) correspond to <span>measured variables, whose values are known precisely. To account for the orientations of the arrows we use the terms "d-separated" and "d-connected" (d connotes "directional"

Original toplevel document

Unknown title
d-SEPARATION WITHOUT TEARS (At the request of many readers) Introduction d-separation is a criterion for deciding, from a given a causal graph, whether a set X of variables is independent of another set Y, given a third set Z. <span>The idea is to associate "dependence" with "connectedness" (i.e., the existence of a connecting path) and "independence" with "unconnected-ness" or "separation". The only twist on this simple idea is to define what we mean by "connecting path", given that we are dealing with a system of directed arrows in which some vertices (those residing in Z) correspond to measured variables, whose values are known precisely. To account for the orientations of the arrows we use the terms "d-separated" and "d-connected" (d connotes "directional"). We start by considering separation between two singleton variables, x and y; the extension to sets of variables is straightforward (i.e., two sets are separated if and only if each el







#forward-backward-algorithm #hmm
The forward–backward algorithm is an inference algorithm for hidden Markov models which computes the posterior marginals of all hidden state variables given a sequence of observations

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

Forward–backward algorithm - Wikipedia
ackward algorithm - Wikipedia Forward–backward algorithm From Wikipedia, the free encyclopedia (Redirected from Forward-backward algorithm) Jump to: navigation, search <span>The forward–backward algorithm is an inference algorithm for hidden Markov models which computes the posterior marginals of all hidden state variables given a sequence of observations/emissions o 1 : t := o 1




#forward-backward-algorithm #hmm
The foreward-backward algorithm makes use of the principle of dynamic programming to compute efficiently the values that are required to obtain the posterior marginal distributions in two passes. The first pass goes forward in time while the second goes backward in time; hence the name forward–backward algorithm.

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

Forward–backward algorithm - Wikipedia
| o 1 : t ) {\displaystyle P(X_{k}\ |\ o_{1:t})} . This inference task is usually called smoothing. <span>The algorithm makes use of the principle of dynamic programming to compute efficiently the values that are required to obtain the posterior marginal distributions in two passes. The first pass goes forward in time while the second goes backward in time; hence the name forward–backward algorithm. The term forward–backward algorithm is also used to refer to any algorithm belonging to the general class of algorithms that operate on sequence models in a forward–backward manner. I




Flashcard 1739077061900

Tags
#forward-backward-algorithm #hmm
Question
The forward–backward algorithm is an inference algorithm for hidden Markov models which computes [...] given a sequence of observations
Answer
the posterior marginals of all hidden state variables


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 forward–backward algorithm is an inference algorithm for hidden Markov models which computes the posterior marginals of all hidden state variables given a sequence of observations

Original toplevel document

Forward–backward algorithm - Wikipedia
ackward algorithm - Wikipedia Forward–backward algorithm From Wikipedia, the free encyclopedia (Redirected from Forward-backward algorithm) Jump to: navigation, search <span>The forward–backward algorithm is an inference algorithm for hidden Markov models which computes the posterior marginals of all hidden state variables given a sequence of observations/emissions o 1 : t := o 1







Flashcard 1739078634764

Tags
#forward-backward-algorithm #hmm
Question
The forward–backward algorithm computes the posterior marginals of all hidden state variables given [...]
Answer
a sequence of observations


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 forward–backward algorithm is an inference algorithm for hidden Markov models which computes the posterior marginals of all hidden state variables given a sequence of observations

Original toplevel document

Forward–backward algorithm - Wikipedia
ackward algorithm - Wikipedia Forward–backward algorithm From Wikipedia, the free encyclopedia (Redirected from Forward-backward algorithm) Jump to: navigation, search <span>The forward–backward algorithm is an inference algorithm for hidden Markov models which computes the posterior marginals of all hidden state variables given a sequence of observations/emissions o 1 : t := o 1







Flashcard 1739080994060

Tags
#forward-backward-algorithm #hmm
Question
The foreward-backward algorithm makes use of the principle of [...] in its two passes.
Answer
dynamic programming


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 foreward-backward algorithm makes use of the principle of dynamic programming to compute efficiently the values that are required to obtain the posterior marginal distributions in two passes. The first pass goes forward in time while the second goes backward in t

Original toplevel document

Forward–backward algorithm - Wikipedia
| o 1 : t ) {\displaystyle P(X_{k}\ |\ o_{1:t})} . This inference task is usually called smoothing. <span>The algorithm makes use of the principle of dynamic programming to compute efficiently the values that are required to obtain the posterior marginal distributions in two passes. The first pass goes forward in time while the second goes backward in time; hence the name forward–backward algorithm. The term forward–backward algorithm is also used to refer to any algorithm belonging to the general class of algorithms that operate on sequence models in a forward–backward manner. I







Flashcard 1739082566924

Tags
#forward-backward-algorithm #hmm
Question
The foreward-backward algorithm obtain [...] in two passes.
Answer
the posterior marginal 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 foreward-backward algorithm makes use of the principle of dynamic programming to compute efficiently the values that are required to obtain the posterior marginal distributions in two passes. The first pass goes forward in time while the second goes backward in time; hence the name forward–backward algorithm.

Original toplevel document

Forward–backward algorithm - Wikipedia
| o 1 : t ) {\displaystyle P(X_{k}\ |\ o_{1:t})} . This inference task is usually called smoothing. <span>The algorithm makes use of the principle of dynamic programming to compute efficiently the values that are required to obtain the posterior marginal distributions in two passes. The first pass goes forward in time while the second goes backward in time; hence the name forward–backward algorithm. The term forward–backward algorithm is also used to refer to any algorithm belonging to the general class of algorithms that operate on sequence models in a forward–backward manner. I








Prerequisite Reading Demand and Supply Analysis: Introduction
#has-images #prerequisite-session #reading-dildo

In a general sense, economics is the study of production, distribution, and consumption and can be divided into two broad areas of study: macroeconomics and microeconomics. Macroeconomics deals with aggregate economic quantities, such as national output and national income. Macroeconomics has its roots in microeconomics, which deals with markets and decision making of individual economic units, including consumers and businesses. Microeconomics is a logical starting point for the study of economics.

This reading focuses on a fundamental subject in microeconomics: demand and supply analysis. Demand and supply analysis is the study of how buyers and sellers interact to determine transaction prices and quantities. As we will see, prices simultaneously reflect both the value to the buyer of the next (or marginal) unit and the cost to the seller of that unit. In private enterprise market economies, which are the chief concern of investment analysts, demand and supply analysis encompasses the most basic set of microeconomic tools.

Traditionally, microeconomics classifies private economic units into two groups: consumers (or households) and firms. These two groups give rise, respectively, to the theory of the consumer and theory of the firm as two branches of study. The theory of the consumer deals with consumption (the demand for goods and services) by utility-maximizing individuals (i.e., individuals who make decisions that maximize the satisfaction received from present and future consumption). The theory of the firm deals with the supply of goods and services by profit-maximizing firms. The theory of the consumer and the theory of the firm are important because they help us understand the foundations of demand and supply. Subsequent readings will focus on the theory of the consumer and the theory of the firm.

Investment analysts, particularly equity and credit analysts, must regularly analyze products and services, their costs, prices, possible substitutes, and complements, to reach conclusions about a company’s profitability and business risk (risk relating to operating profits). Furthermore, unless the analyst has a sound understanding of the demand and supply model of markets, he or she cannot hope to forecast how external events—such as a shift in consumer tastes or changes in taxes and subsidies or other intervention in markets—will influence a firm’s revenue, earnings, and cash flows.

Having grasped the tools and concepts presented in this reading, the reader should also be able to understand many important economic relations and facts and be able to answer questions, such as:

  • Why do consumers usually buy more when the price falls? Is it irrational to violate this “law of demand”?

  • What are appropriate measures of how sensitive the quantity demanded or supplied is to changes in price, income, and prices of other

...

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




Flashcard 1739110616332

Question
No single investor can hold shares in an Indian Stock Exchange beyond a limit
Answer
5%.


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







Government securities are coupon bearing instruments which are issued by RBI on behalf of Government of India. Government securities have maturity dates ranging from less than 1 year to a max of 30 year.

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

pdf

cannot see any pdfs




Debentures are bonds issued by a company. It has fixed rate of interest usually payable half-yearly, on specific dates and the principal amount repayable on a particular date on redemption of debenture. It is an unsecured debt

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

pdf

cannot see any pdfs




Flashcard 1739116645644

Question
Debentures are bonds issued by a company. It has [...] rate of interest usually payable half-yearly, on specific dates and the principal amount repayable on a particular date on redemption of debenture. It is an unsecured debt
Answer
fixed


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
Debentures are bonds issued by a company. It has fixed rate of interest usually payable half-yearly, on specific dates and the principal amount repayable on a particular date on redemption of debenture. It is an unsecured de

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1739117694220

Question
Debentures are bonds issued by a company. It has fixed rate of interest usually payable half-yearly, on specific dates and the [...] amount repayable on a particular date on redemption of debenture. It is an unsecured debt
Answer
principal


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
Debentures are bonds issued by a company. It has fixed rate of interest usually payable half-yearly, on specific dates and the principal amount repayable on a particular date on redemption of debenture. It is an unsecured debt

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1739118742796

Question
Debentures are bonds issued by a company. It has fixed rate of interest usually payable half-yearly, on specific dates and the principal amount repayable on a particular date on redemption of debenture. It is an [...] debt
Answer
unsecured


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
Debentures are bonds issued by a company. It has fixed rate of interest usually payable half-yearly, on specific dates and the principal amount repayable on a particular date on redemption of debenture. It is an <span>unsecured debt <span><body><html>

Original toplevel document (pdf)

cannot see any pdfs







The Mumbai Interbank Offered Rate (MIBOR) is calculated everyday by the National Stock Exchange of India (NSEIL) as a weighted average of lending rates of a group of banks, on funds lent to first-class borrowers. 26. It is the interest rate at which banks can borrow funds, in marketable size, from other banks in the Indian interbank market. 27. The MIBOR was launched on June 15, 1998 by the Committee for the Development of the Debt Market, as an overnight rate.

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

pdf

cannot see any pdfs




LIBOR or ICE LIBOR (previously BBA LIBOR) is a benchmark rate that some of the world’s leading banks charge each other for short-term loans.
22. It stands for Intercontinental Exchange London Interbank Offered Rate and serves as the first step to calculating interest rates on various loans throughout the world.

23. LIBOR is administered by the ICE Benchmark Administration (IBA), and is based on five currencies: U.S. dollar (USD), Euro (EUR), pound sterling (GBP), Japanese yen (JPY) and Swiss franc (CHF).
24. It serves seven different maturities: overnight, one week, and 1, 2, 3, 6 and 12 months. There are a total of 35 different LIBOR rates each business day. The most commonly quoted rate is the three- month U.S. dollar rate.

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

pdf

cannot see any pdfs




Features of Government Securities 1. Issued at face value. 2. No default risk as the securities carry sovereign guarantee. 3. Ample liquidity as the investor can sell the security in the secondary market. 4. Interest payment on a half yearly basis on face value. 5. No tax deducted at source. 6. Can be held in Demat form

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

pdf

cannot see any pdfs




#rbi
The Reserve Bank of India was established on April 1, 1935 in accordance with the provisions of the Reserve Bank of India Act, 1934.

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

Reserve Bank of India - About Us
Training Establishments Subsidiaries Establishment <span>The Reserve Bank of India was established on April 1, 1935 in accordance with the provisions of the Reserve Bank of India Act, 1934. The Central Office of the Reserve Bank was initially established in Calcutta but was permanently moved to Mumbai in 1937. The Central Offi




The NSEIL launched the 14-day MIBOR on November 10, 1998, and the one month and three month MIBORs on December 1, 1998. 29. Since the launch, MIBOR rates have been used as benchmark rates for the majority of money market deals made in India.

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

pdf

cannot see any pdfs




FII can invest up to 49 % in Stock Exchange in India.

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

pdf

cannot see any pdfs




Corporatization is the process of converting the organizational structure of the Stock Exchange from a non-corporate to a corporate structure. 4. Demutualization refers to the transition process of a Stock Exchange from a mutually owned association to a shareholders-owned company.

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

pdf

cannot see any pdfs




Commercial papers are borrowing of a company from the market. These money market instruments are issued for 90 days.

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

pdf

cannot see any pdfs




Any company making a public issue or a listed company making a RI of a value of more than Rs 50 lacs is required to file a draft offer document with SEBI for its observations. This observation period is only 3 months.

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

pdf

cannot see any pdfs




DIP stands for Disclosure and Investor Protection guidelines

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

pdf

cannot see any pdfs




Offer document means prospectus in case of public issue. 20. Offer document means an offer for sale and letter of offer in case of a RI. 21. Offer documents are filed with Registrar of Companies and Stock Exchanges. 22. A draft offer document means the offer document in a draft stage. 23. The draft offer documents are filed with SEBI. 24. The period of filing draft offer document is at least 21 days prior to that of offer document.

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

pdf

cannot see any pdfs




RHP (Red Herring Prospectus) is a prospectus which doesn’t have details of either price of number of shares being offered or the amount of issue. But the number of shares and the upper and lower price bands are disclosed. 26. In case of FPO, the RHP can be filed with Registrar of Companies without the price band. The price band is notified one day prior to the opening of the issue by way of an advertisement.

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

pdf

cannot see any pdfs




Flashcard 1739151248652

Question
Commercial papers are borrowing of a company from the market. These are issued for [...] days.
Answer
90


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
Commercial papers are borrowing of a company from the market. These money market instruments are issued for 90 days.

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1739153607948

Question
Any company making a public issue or a listed company making a RI of a value of more than Rs 50 lacs is required to file a draft offer document with SEBI for its observations. This observation period is only [...] months.
Answer
3


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
Any company making a public issue or a listed company making a RI of a value of more than Rs 50 lacs is required to file a draft offer document with SEBI for its observations. This observation period is only 3 months. <body><html>

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1739155180812

Question
Any company making a public issue or a listed company making a RI of a value of more than Rs [...] is required to file a draft offer document with SEBI for its observations. This observation period is only 3 months.
Answer
50 lacs


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
Any company making a public issue or a listed company making a RI of a value of more than Rs 50 lacs is required to file a draft offer document with SEBI for its observations. This observation period is only 3 months.

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1739156753676

Question
RHP (Red Herring Prospectus) is a prospectus which doesn’t have details of either [...] of number of shares being offered or the amount of issue. But the number of shares and the upper and lower price bands are disclosed. 26. In case of FPO, the RHP can be filed with Registrar of Companies without the price band. The price band is notified one day prior to the opening of the issue by way of an advertisement.
Answer
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
RHP (Red Herring Prospectus) is a prospectus which doesn’t have details of either price of number of shares being offered or the amount of issue. But the number of shares and the upper and lower price bands are disclosed. 26. In case of FPO, the RHP can be filed

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1739158326540

Question
RHP (Red Herring Prospectus) is a prospectus which doesn’t have details of either price of number of shares being offered or the [...]. But the number of shares and the upper and lower price bands are disclosed. 26. In case of FPO, the RHP can be filed with Registrar of Companies without the price band. The price band is notified one day prior to the opening of the issue by way of an advertisement.
Answer
amount of issue


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
RHP (Red Herring Prospectus) is a prospectus which doesn’t have details of either price of number of shares being offered or the amount of issue. But the number of shares and the upper and lower price bands are disclosed. 26. In case of FPO, the RHP can be filed with Registrar of Companies without the price band. The

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1739159899404

Question
RHP (Red Herring Prospectus) is a prospectus which doesn’t have details of either price of number of shares being offered or the amount of issue. But the [...] and the upper and lower price bands are disclosed. 26. In case of FPO, the RHP can be filed with Registrar of Companies without the price band. The price band is notified one day prior to the opening of the issue by way of an advertisement.
Answer
number of shares


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
RHP (Red Herring Prospectus) is a prospectus which doesn’t have details of either price of number of shares being offered or the amount of issue. But the number of shares and the upper and lower price bands are disclosed. 26. In case of FPO, the RHP can be filed with Registrar of Companies without the price band. The price band is notified on

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1739161472268

Question
RHP (Red Herring Prospectus) is a prospectus which doesn’t have details of either price of number of shares being offered or the amount of issue. But the number of shares and the upper and lower price bands are disclosed. 26. In case of [...], the RHP can be filed with Registrar of Companies without the price band. The price band is notified one day prior to the opening of the issue by way of an advertisement.
Answer
FPO


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
ring Prospectus) is a prospectus which doesn’t have details of either price of number of shares being offered or the amount of issue. But the number of shares and the upper and lower price bands are disclosed. 26. In case of <span>FPO, the RHP can be filed with Registrar of Companies without the price band. The price band is notified one day prior to the opening of the issue by way of an advertisement. </spa

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1739163045132

Question
RHP (Red Herring Prospectus) is a prospectus which doesn’t have details of either price of number of shares being offered or the amount of issue. But the number of shares and the upper and lower price bands are disclosed. 26. In case of FPO, the RHP can be filed with [...] without the price band. The price band is notified one day prior to the opening of the issue by way of an advertisement.
Answer
Registrar of Companies


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
s which doesn’t have details of either price of number of shares being offered or the amount of issue. But the number of shares and the upper and lower price bands are disclosed. 26. In case of FPO, the RHP can be filed with <span>Registrar of Companies without the price band. The price band is notified one day prior to the opening of the issue by way of an advertisement. <span><body><html>

Original toplevel document (pdf)

cannot see any pdfs








Prerequisite Demand and Supply Analysis: Consumer Demand
#has-images #prerequisite-session #reading-codo

By now it should be clear that economists are model builders. In the previous reading, we examined one of their most fundamental models, the model of demand and supply. And as we have seen, models begin with simplifying assumptions and then find the implications that can then be compared to real-world observations as a test of the model’s usefulness. In the model of demand and supply, we assumed the existence of a demand curve and a supply curve, as well as their respective negative and positive slopes. That simple model yielded some very powerful implications about how markets work, but we can delve even more deeply to explore the underpinnings of demand and supply. In this reading, we examine the theory of the consumer as a way of understanding where consumer demand curves originate. In a subsequent reading, the origins of the supply curve are sought in presenting the theory of the firm.

This reading is organized as follows:

Section 2 describes consumer choice theory in more detail.

Section 3 introduces utility theory, a building block of consumer choice theory that provides a quantitative model for a consumer’s preferences and tastes.

Section 4 surveys budget constraints and opportunity sets. Section 5 covers the determination of the consumer’s bundle of goods and how that may change in response to changes in income and prices.

Section 6 examines substitution and income effects for different types of goods.

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





Prerequisite Demand and Supply Analysis: The firm
#has-images #introduction #prerequisite-session #reading-saco-de-polipropileno

In studying decision making by consumers and businesses, microeconomics gives rise to the theory of the consumer and theory of the firm as two branches of study.

The theory of the consumer is the study of consumption—the demand for goods and services—by utility-maximizing individuals. The theory of the firm, the subject of this reading, is the study of the supply of goods and services by profit-maximizing firms. Conceptually, profit is the difference between revenue and costs. Revenue is a function of selling price and quantity sold, which are determined by the demand and supply behavior in the markets into which the firm sells/provides its goods or services. Costs are a function of the demand and supply interactions in resource markets, such as markets for labor and for physical inputs. The main focus of this reading is the cost side of the profit equation for companies competing in market economies under perfect competition. A subsequent reading will examine the different types of markets into which a firm may sell its output.

The study of the profit-maximizing firm in a single time period is the essential starting point for the analysis of the economics of corporate decision making. Furthermore, with the attention given to earnings by market participants, the insights gained by this study should be practically relevant. Among the questions this reading will address are the following:

  • How should profit be defined from the perspective of suppliers of capital to the firm?

  • What is meant by factors of production?

  • How are total, average, and marginal costs distinguished, and how is each related to the firm’s profit?

  • What roles do marginal quantities (selling prices and costs) play in optimization?

This reading is organized as follows:

Section 2 discusses the types of profit measures, including what they have in common, how they differ, and their uses and definitions.

Section 3 covers the revenue and cost inputs of the profit equation and the related topics of breakeven analysis, shutdown point of operation, market entry and exit, cost structures, and scale effects. In addition, the economic outcomes related to a firm’s optimal supply behavior over the short run and long run are presented in this section.

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