Divergence measures and message passing Export

@techreport{citeulike:3757132, abstract = {This paper presents a unifying view of message-passing algorithms, as methods to approximate a complex Bayesian network by a simpler network with minimum information divergence. In this view, the difference between mean-field methods and belief propagation is not the amount of structure they model, but only the measure of loss they minimize (`exclusive' versus `inclusive' Kullback-Leibler divergence). In each case, message-passing arises by minimizing a localized version of the divergence, local to each factor. By examining these divergence measures, we can intuit the types of solution they prefer (symmetry-breaking, for example) and their suitability for different tasks. Furthermore, by considering a wider variety of divergence measures (such as alpha-divergences), we can achieve different complexity and performance goals.}, author = {Minka, Thomas}, citeulike-article-id = {3757132}, citeulike-linkout-0 = {http://research.microsoft.com/~minka/papers/message-passing/}, institution = {Microsoft Research}, keywords = {bayesian, divergence, message-passing, network}, posted-at = {2009-02-09 12:25:53}, priority = {2}, title = {Divergence measures and message passing}, url = {http://research.microsoft.com/~minka/papers/message-passing/}, year = {2005} }

TY - RPRT ID - citeulike:3757132 L3 - citeulike-article-id:3757132 N2 - This paper presents a unifying view of message-passing algorithms, as methods to approximate a complex Bayesian network by a simpler network with minimum information divergence. In this view, the difference between mean-field methods and belief propagation is not the amount of structure they model, but only the measure of loss they minimize (`exclusive' versus `inclusive' Kullback-Leibler divergence). In each case, message-passing arises by minimizing a localized version of the divergence, local to each factor. By examining these divergence measures, we can intuit the types of solution they prefer (symmetry-breaking, for example) and their suitability for different tasks. Furthermore, by considering a wider variety of divergence measures (such as alpha-divergences), we can achieve different complexity and performance goals. TI - Divergence measures and message passing KW - bayesian KW - divergence KW - message-passing KW - network AU - Minka, Thomas PY - 2005/// UR - http://research.microsoft.com/~minka/papers/message-passing/ ER -