Graphical models: parameter learning

“Graphical models” combine graph theory and probability theory to provide a general framework for representing models in which a number of variables interact. Graphical models trace their origins to many different fields and have been applied in wide variety of settings: for example, to develop probabilistic expert systems, to understand neural network models, to infer trait inheritance in geneologies, to model images, to correct errors in digital communication, or to solve complex decision problems. Remarkably, the same formalisms and algorithms can be applied to this wide range of problems. Each node in the graph represent a random variable (or more generally a set of random variables). The pattern of edges in the graph represents the qualitative dependencies between the variables; the absence of an edge between two nodes means that any statistical dependency between these two variables is mediated via some other variable or set of variables. The quantitative dependencies between variables which are connected via edges are specified via parameterized conditional distributions, or more generally non-negative “potential functions”. The pattern of edges and the potential functions together specify a joint probability distribution over all the variables in the graph. We refer to the pattern of edges as the structure of the graph, while the parameters of the potential functions simply as the parameters of the graph. In this chapter, we assume that the structure of the graph is given, and that our goal is to learn the parameters of the graph from data. Solutions to the problem of learning the graph structure from data are given in GRAPHICAL MODELS, STRUCTURE LEARNING. We briefly review some of the notation from PROBABILISTIC INFERENCE IN GRAPHICAL MODELS which we will need to cover parameter learning in graphical models; we assume that the reader is familiar with the contents

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