Markov chain monte carlo using tree-based priors on model structure

Authors:
Nicos Angelopoulos;James Cussens
Affiliations:
Department of Computer Science, University of York, Heslington, York, UK;Department of Computer Science, University of York, Heslington, York, UK
Venue:
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Year:
2001

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Abstract

We present a general framework for defining priors on model structure and sampling from the posterior using the Metropolis-Hastings algorithm. The key ideas are that structure priors are defined via a probability tree and that the proposal distribution for the Metropolis-Hastings algorithm is defined using the prior, thereby defining a cheaply computable acceptance probability. We have applied this approach to Bayesian net structure learning using a number of priors and proposal distributions. Our results show that these must be chosen appropriately for this approach to be successful.