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General Departmental Seminar Series


Markov Chain Monte Carlo Using Tree-based Priors on Model Structure

James Cussens, Department of Computer Science
University of York

Tuesday, May 8, 2001, 4:00-5:00pm

1221 Computer Sciences Statistics Center
1210 W. Dayton St.

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.

James Cussens is currently a visiting professor in the Department of Biostatistics and Medical Informatics at UW-Madison.


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