Learning Probabilistic Graphical Models

Abstract

Probabilistic Graphical Models (PGM) are a class of statistical models that use a graph structure over a set of variables to encode independence relations between those variables. By augmenting the graph by local parameters, a PGM allows for a compact representation of a joint probability distribution over the variables of the graph, which allows for efficient inference algorithms. PGMs are often used for modeling physical and biological systems, and such models are then in turn used to both answer probabilistic queries concerning the variables and to represent certain causal and/or statistical relations in the domain. In this chapter, the authors give an overview of common techniques used for automatic construction of such models from a dataset of observations (usually referred to as learning), and they also review some important applications. The chapter guides the reader to the relevant literature for further study.