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.

Appears in Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence (UAI2000)

References in corpus (1)

• Loglinear models for first-order probabilistic reasoning

Cited by in corpus (4)

• Adaptive MCMC-Based Inference in Probabilistic Logic Programs
• CP-logic: A Language of Causal Probabilistic Events and Its Relation to Logic Programming
• Deriving a Stationary Dynamic Bayesian Network from a Logic Program with Recursive Loops
• Markov Chain Monte Carlo using Tree-Based Priors on Model Structure