Advanced PGMs

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Generative Models | Continue |
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Description: # Generative Models Based on application, machine learning models can be broadly divided into two categories - Discriminative and Generative. A Discriminative Model is one which classifies, seggregates or differentiates the data. A Generative Model is a model which given a training dataset generates new sample data following the same distribution. It is a class of models belonging to unsupervised classification. <img src="../images/types_of_models.png">

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Description: # Generative Models Based on application, machine learning models can be broadly divided into two categories - Discriminative and Generative. A Discriminative Model is one which classifies, seggregates or differentiates the data. A Generative Model is a model which given a training dataset generates new sample data following the same distribution. It is a class of models belonging to unsupervised classification. <img src="../images/types_of_models.png">

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Markov Network | Continue |
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Description: # Markov Network ## Introduction to Markov Random Fields A Markov Network or Markov Random Field is an undirected graph where the nodes represent the random variables and the edges represent the connection between the random variables. These graphs could be cyclic unlike the bayesian networks. <br>

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Description: # Markov Network ## Introduction to Markov Random Fields A Markov Network or Markov Random Field is an undirected graph where the nodes represent the random variables and the edges represent the connection between the random variables. These graphs could be cyclic unlike the bayesian networks. <br>

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Probablisitic Inferences in Graphical Models | Continue |
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Description: # Probablisitic Inferences in Graphical Models The advantage of PGMs over standard probabilistic ways of determining conditional probability distributions. The advantages of PGMs is that it allows expression of the graphical model as a joint distribution over all random variables. This then allows us to marginalize over the random variables to determine quantities of interest. The joint probability distribution associated with a given graph can be expressed as a product over potential functions associated with subsets of nodes in the graph. ## Bayesian Networks

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Description: # Probablisitic Inferences in Graphical Models The advantage of PGMs over standard probabilistic ways of determining conditional probability distributions. The advantages of PGMs is that it allows expression of the graphical model as a joint distribution over all random variables. This then allows us to marginalize over the random variables to determine quantities of interest. The joint probability distribution associated with a given graph can be expressed as a product over potential functions associated with subsets of nodes in the graph. ## Bayesian Networks

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Markov Models | Continue |
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Description: # Markov Models ## Introduction to Markov Model A Markov Model is a stochastic model that models sequential or temporal data. In other words, it is used for modelling events that may occur repeatedly over time or predictable events that occur over time.

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Description: # Markov Models ## Introduction to Markov Model A Markov Model is a stochastic model that models sequential or temporal data. In other words, it is used for modelling events that may occur repeatedly over time or predictable events that occur over time.

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Boltzmann_Machines | Continue |
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Description: # Boltzmann_Machines A Boltzmann machine is a stochastic, recurrent neural network, consisting of atleast two layers - visible and hidden. The visible and hidden layers consist visible (**V<sub>1</sub>, V<sub>2</sub>, V<sub>3</sub>...V<sub>n</sub>**) and hiddens nodes (**H<sub>1</sub>, H<sub>2</sub>, H<sub>3</sub>...H<sub>m</sub>**). Each node in the visible layer is connected to every node in the hidden layer. A Restricted Boltzmann machine is a type of Boltzmann machine where visible-visible and hidden-hidden relationships, i.e., relationships within a layer are restricted. Restriction reduces the number of connections between input nodes (visible layer is the input nodes layer) and hidden nodes and hence enables practical application of a Boltzmann machine. <img src="../images/BM.png", style="height:50vh;">

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Description: # Boltzmann_Machines A Boltzmann machine is a stochastic, recurrent neural network, consisting of atleast two layers - visible and hidden. The visible and hidden layers consist visible (**V<sub>1</sub>, V<sub>2</sub>, V<sub>3</sub>...V<sub>n</sub>**) and hiddens nodes (**H<sub>1</sub>, H<sub>2</sub>, H<sub>3</sub>...H<sub>m</sub>**). Each node in the visible layer is connected to every node in the hidden layer. A Restricted Boltzmann machine is a type of Boltzmann machine where visible-visible and hidden-hidden relationships, i.e., relationships within a layer are restricted. Restriction reduces the number of connections between input nodes (visible layer is the input nodes layer) and hidden nodes and hence enables practical application of a Boltzmann machine. <img src="../images/BM.png", style="height:50vh;">

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