@book {getoor:prm-ch-srl-book07, title = {Probabilistic Relational Models}, series = {An Introduction to Statistical Relational Learning}, volume = {1}, year = {2007}, pages = {129--174}, publisher = {MIT Press}, organization = {MIT Press}, edition = {1}, chapter = {5}, abstract = {

Probabilistic relational models (PRMs) are a rich representation language for structured statistical models. They combine a frame-based logical representation with probabilistic semantics based on directed graphical models (Bayesian networks). This chapter gives an introduction to probabilistic relational models, describing semantics for attribute uncertainty, structural uncertainty, and class uncertainty. For each case, learning algorithms and some sample results are presented.

}, author = {Lise Getoor and Nir Friedman and Daphne Koller and Avi Pfeffer and Benjamin Taskar}, editor = {Lise Getoor and Benjamin Taskar} }