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Assignment on Probabilistic Graphical Model

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Added on  2019-10-09

Assignment on Probabilistic Graphical Model

   Added on 2019-10-09

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Bayesian Network helps utilize the probabilities in the Artificial Intelligence. It is a form of probabilistic graphical model. The model shows the dependencies among the variables. One of the examples of the model can be the representation of diseases and the symptoms. If the symptoms are available with the individual then the individual can use the tool to understand the likeliness of various diseases. The representation of the network is through arrows and nodes. The nodes present in the model denote the quantities that can be identified by the observers. In some cases, there are variables that are not known completely but can be inferred from the situation. Such variables are called latent variables, which are also used as the nodes (Jensen, 1996). Moreover, there are hypothetical variables defined by the individuals to understand the situations that are also denoted on the nodes of the models. The diagram given below shows the simple model of the network consisting of nodes and the dependencies shown through arrows:The variables shown through the nodes are represented as X = X1+X2+...Xn and the arcs that are shown in the diagram above is represented by XiXj. The arcs shows the direct dependencies among the variables they are connected. One of the important aspects that are used in this network is the conditional probability. The conditional probability shows the strengths of the dependencies. However, one thing that should be kept in consideration while forming the nodes and arcs is that the directed cycles should not there (Nielsen 7 Jensen,
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2009 ). If an individual wants to return to a node by moving through the directed arcs, then it would not reach feasible solution. As per the inference is concerned, there are three of these within BN. They are given below:Inferring unobserved variablesDue to the presence of all the observed variables in the Bayesian Network, it becomes easier to infer the variables that are unobserved. Answering the queries with the help of probability becomes easier (Murphy, 2002). Parameter learningParameters are used to show the likelihood of one cause over another. The parameters are generally unknown and require estimation.Structure learningThe structures are defined in BNs with the help of experts and then inferences are derived. Data are used to form the structure as in most cases it becomes challenging due to its complexity.The construction of Bayesian Networ requires understanding few steps. These steps are givenbelow:Nodes and valuesThis is the first step where the individual tries to understand the variables that are into play. There are some questions that are considered while selecting the variables for the nodes such as the values that are applicable, or the state in which they are, and others. The discrete nodeshave been preferred here for discussion (Heckerman, 1998). Some of general discrete nodes types are Boolean nodes, ordered nodes, and integral nodes. The Boolean nodes refer to the
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