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.

• Type: Conference
• Year: 2001
• Where: UAI
• Authors: Nicos Angelopoulos, James Cussens
• Keywords: Priors, Proposal Distributions, Structure Priors, UAI 2001, UAI 2008