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

2013/01/10 by Nicos Angelopoulos, Angelopoulos, Nicos, James Cussens +1
Computer Science · Mathematics · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Markov Chains and Monte Carlo Methods #Statistical Methods and Inference #cs.AI

paper · pdf · doi:10.48550/arxiv.1301.2254

Appears in Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI2001)

arxiv created 2013/01/10 · openalex publication_date 2013/01/10 · arxiv updated 2013/01/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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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