Probabilistic Relational Models

TitleProbabilistic Relational Models
Publication TypeBook
Year of Publication2007
AuthorsGetoor, L, Friedman, N, Koller, D, Pfeffer, A, Taskar, B
Series EditorGetoor, L, Taskar, B
Series TitleAn Introduction to Statistical Relational Learning
Volume1
Edition1
Chapter5
Pagination129--174
PublisherMIT Press
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.