Uncertainty in Artificial Intelligence
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Stochastic Logic Programs: Sampling, Inference and Applications
James Cussens
Abstract:
Algorithms for exact and approximate inference in stochastic logic programs (SLPs) are presented, based respectively, on variable elimination and importance sampling. We then show how SLPs can be used to represent prior distributions for machine learning, using (i) logic programs and (ii) Bayes net structures as examples. Drawing on existing work in statistics, we apply the Metropolis-Hasting algorithm to construct a Markov chain which samples from the posterior distribution. A Prolog implementation for this is described. We also discuss the possibility of constructing explicit representations of the posterior.
Keywords: loglinear models, importance sampling, MCMC, logic programming
Pages: 115-122
PS Link: ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/uai00.ps.gz
PDF Link: /papers/00/p115-cussens.pdf
BibTex:
@INPROCEEDINGS{Cussens00,
AUTHOR = "James Cussens ",
TITLE = "Stochastic Logic Programs: Sampling, Inference and Applications",
BOOKTITLE = "Proceedings of the Sixteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-00)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2000",
PAGES = "115--122"
}


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