Bayesian Model Comparison by Monte Carlo Chaining

Part of Advances in Neural Information Processing Systems 9 (NIPS 1996)

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David Barber, Christopher Bishop


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.

1 Bayesian Model Comparison

In a Bayesian treatment of statistical inference, our state of knowledge of the values of the parameters w in a model M is described in terms of a probability distribution function. Initially this is chosen to be some prior distribution p(wIM), which can be combined with a likelihood function p( Dlw, M) using Bayes' theorem to give a posterior distribution p(wID, M) in the form