Stochastic logic programs: Sampling, inference and applications

James Cussens

Stochastic logic programs: Sampling, inference and applications

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	Proceedings of the Sixteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI--2000)
Place of Publication	San Francisco, CA
Publisher	MORGAN KAUFMANN PUB INC
Pages	115-122
Number of pages	8
Publication status	Published - 2000

Access to Document

ftp://ftp.cs.york.ac.uk/pub/aig/Papers/james.cussens/uai00.ps.gz

Cite this

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title = "Stochastic logic programs: Sampling, inference and applications",

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.",

author = "James Cussens",

year = "2000",

language = "English",

pages = "115--122",

booktitle = "Proceedings of the Sixteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI--2000)",

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AU - Cussens, James

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N2 - 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.

AB - 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.

M3 - Conference contribution

SP - 115

EP - 122

BT - Proceedings of the Sixteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI--2000)

PB - MORGAN KAUFMANN PUB INC

CY - San Francisco, CA

ER -