P Probabilistic Graphical Models
نویسنده
چکیده
Bayesian network (BN) A directed graph whose nodes represent variables, and edges represent influences. Together with conditional probability distributions, a Bayesian network represents the joint probability distribution of its variables. Conditional probability distribution Assignment of probabilities to all instances of a set of variables when the value of one or more variables is known. Conditional random field (CRF) A partially directed graph that represents a conditional distribution. Factor graph A type of parameterization of PGMs in the form of bipartite graphs of factor nodes and variable nodes, where a factor node indicates that the variable nodes is connected to form a clique in a PGM. Graph A set of nodes and edges, where edges connect pairs of nodes. Inference Process of answering queries using the distribution as the model of the world. Joint probability distribution Assignment of probabilities to each instance of a set of random variables. Log-linear model A Markov network represented using features and energy functions. Markov network (MN) An undirected graph whose nodes represent variables, and edges represent influences. Together with factors defined over subsets of variables, a Markov network represents the joint probability distribution of its variables. Markov random field (MRF) Synonymous with Markov network. Term more commonly used in computer vision.
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