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2001

Markov Chain Monte Carlo using Tree-Based Priors on Model Structure

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Markov Chain Monte Carlo using Tree-Based Priors on Model Structure
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.
Nicos Angelopoulos, James Cussens
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2001
Where UAI
Authors Nicos Angelopoulos, James Cussens
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