An Analysis of Gibbs Sampling for Probabilistic Logic Programs

Markov Chain Monte Carlo (MCMC) is one of the most used families of algorithms based on sampling. They allow to sample from the posterior distribution when direct sampling from it is infeasible, due to the complexity of the distribution itself. Gibbs sampling is one of these algorithm that has been applied in many situations. In this paper we compare an implementation of Gibbs sampling for Probabilistic Logic Programs on several datasets, in order to better understand its performance. For all the experiments we compute the convergence time, execution time and population standard deviation of the samples.