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 adding article you MUST have a user

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

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Under the completed contract method, what does it mean for the contract to be substantially finished?
That the remaining costs and potential risks are insignificant in amount

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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.

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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

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

1/(52) = 1/25

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Annotation 1732138896652

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

Annotation 1738844278028

 #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.

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

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

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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>

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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

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the Banach fixed-point theorem is also known as [...]
the contraction mapping theorem

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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

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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

Annotation 1738858695948

 #metric-space 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.

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

Annotation 1738860268812

 #metric-space 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 x0 in X and define a sequence {xn} by xn = T(xn−1), then xn → x* .

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

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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.

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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.

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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

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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.

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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.

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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

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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

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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* . <

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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

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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* .
an arbitrary element x0 in X

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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>

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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

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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* .
xn = T(xn−1)

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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>

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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

Annotation 1739017293068

 #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.

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

Annotation 1739022798092

 #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.

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

Annotation 1739029613836

 #bayesian-network 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.

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

Annotation 1739031710988

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

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

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In a graphical model, a graph expresses [...] between random variables.
the conditional dependence structure

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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.

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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

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

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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

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

is the set of parents of node .

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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>

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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

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

a product of conditional distributions

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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

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A Markov random field is a model over [...].

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

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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

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A [...] is a model over an undirected graph.
Markov random field

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

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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

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In Bayesian network graphs [...] represent variables
nodes

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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

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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

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Graphically, Bayesian networks use [...] to represent conditional dependencies
Edges

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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

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nodes that are not connected represent variables that are [...].

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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

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In a Bayesian network each node takes [...] as input
a set of values from parent nodes

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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

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In Bayesian networks each node represents a variable with [...]
a probability distribution

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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>

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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

Annotation 1739063954700

 #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.

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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"

Annotation 1739066051852

 #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 we are dealing with a system of directed arrowsin 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").

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

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#d-separation
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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.
connecting path

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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.
measured variables

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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

Annotation 1739072867596

 #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

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

Annotation 1739074964748

 #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.

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

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

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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

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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

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

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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

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

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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

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#forward-backward-algorithm #hmm
Question
The foreward-backward algorithm obtain [...] in two passes.
the posterior marginal distributions

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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

Flashcard 1739110616332

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

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Annotation 1739113237772

 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.

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Annotation 1739115072780

 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

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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
fixed

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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

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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
principal

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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

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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
unsecured

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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>

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Annotation 1739121102092

 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.

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Annotation 1739122674956

 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.

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Annotation 1739125034252

 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

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Annotation 1739135257868

 #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.

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

Annotation 1739138665740

 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.

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Annotation 1739140238604

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

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Annotation 1739141811468

 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.

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Annotation 1739143384332

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

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Annotation 1739144957196

 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.

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Annotation 1739146530060

 DIP stands for Disclosure and Investor Protection guidelines

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Annotation 1739148102924

 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.

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Annotation 1739149675788

 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.

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Flashcard 1739151248652

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

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

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Flashcard 1739153607948

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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.
3

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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>

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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.
50 lacs

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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.

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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.
price

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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

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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.
amount of issue

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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

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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.
number of shares

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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

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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.
FPO

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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

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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.
Registrar of Companies

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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>

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Annotation 1739169598732

 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.