Graphical Models, Exponential Families, and Variational Inference by Martin J Wainwright

Graphical Models, Exponential Families, and Variational Inference by Martin J Wainwright

The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building large-scale multivariate statistical models. Graphical models have become a focus of research in many statistical, computational and mathematical fields, including bioinformatics, communication theory, statistical physics, combinatorial optimization, signal and image processing, information retrieval and statistical machine learning. Many problems that arise in specific instances-including the key problems of computing marginals and modes of probability distributions-are best studied in the general setting. Working with exponential family representations, and exploiting the conjugate duality between the cumulant function and the entropy for exponential families, this book develops general variational representations of the problems of computing likelihoods, marginal probabilities and most probable configurations. It describes how a wide variety of algorithms- among them sum-product, cluster variational methods, expectation-propagation, mean field methods, and max-product-can all be understood in terms of exact or approximate forms of these variational representations. The variational approach provides a complementary alternative to Markov chain Monte Carlo as a general source of approximation methods for inference in large-scale statistical models.

SKU	Unavailable
ISBN 13	9781601981844
ISBN 10	1601981848
Title	Graphical Models, Exponential Families, and Variational Inference
Author	Martin J Wainwright
Series	Foundations And Trends® In Machine Learning
Condition	Unavailable
Binding Type	Paperback
Publisher	now publishers Inc
Year published	2008-12-15
Number of pages	324
Cover note	Book picture is for illustrative purposes only, actual binding, cover or edition may vary.