Stochastic logic programs: Sampling, inference and applications

Research output: Other contribution


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 languageEnglish
Number of pages8
Place of PublicationSan Francisco, CA
Publication statusPublished - 2000


Dive into the research topics of 'Stochastic logic programs: Sampling, inference and applications'. Together they form a unique fingerprint.

Cite this