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

Nicos Angelopoulos; James Cussens

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arXiv · 1301.2254

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

Nicos Angelopoulos·James Cussens

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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 idea is that structure priors are defined via a probability tree and that the proposal mechanism for the Metropolis-Hastings algorithm operates by traversing this tree, thereby defining a cheaply computable acceptance probability. We have applied this approach to Bayesian net structure learning using a number of priors and tree traversal strategies. Our results show that these must be chosen appropriately for this approach to be successful.

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Nicos Angelopoulos, James Cussens. 2013-01-10. Markov Chain Monte Carlo using Tree-Based Priors on Model Structure. https://arxiv.org/abs/1301.2254

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