Bayesian Model Comparison by Monte Carlo Chaining

David Barber, Christopher M. Bishop

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The techniques of Bayesian inference have been applied with great success to many problems in neural computing including evaluation of regression functions, determination of error bars on predictions, and the treatment of hyper-parameters. However, the problem of model comparison is a much more challenging one for which current techniques have significant limitations. In this paper we show how an extended form of Markov chain Monte Carlo, called chaining, is able to provide effective estimates of the relative probabilities of different models. We present results from the robot arm problem and compare them with the corresponding results obtained using the standard Gaussian approximation framework.
Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 9 (NIPS 1996)
EditorsM.C. Mozer, M.I. Jordan, T. Petsche
PublisherMIT Press
Number of pages7
Publication statusPublished - 1997


Dive into the research topics of 'Bayesian Model Comparison by Monte Carlo Chaining'. Together they form a unique fingerprint.

Cite this