  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.
