Uncertainty in Artificial Intelligence
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Markov Chain Monte Carlo using Tree-Based Priors on Model Structure
Nicos Angelopoulos, James Cussens
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.
Pages: 16-23
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PDF Link: /papers/01/p16-angelopoulos.pdf
AUTHOR = "Nicos Angelopoulos and James Cussens",
TITLE = "Markov Chain Monte Carlo using Tree-Based Priors on Model Structure",
BOOKTITLE = "Proceedings of the Seventeenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-01)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2001",
PAGES = "16--23"

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