By the same authors

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution



Publication details

Title of host publicationProceedings of the Seventeenth Annual Conference on Uncertainty in Artificial Intelligence (UAI--2001)
DatePublished - 1 Aug 2001
Number of pages8
Place of PublicationSeattle
EditorsJack Breese, Daphne Koller
Original languageEnglish


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.

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