%0 Journal Article
%T Particle Gibbs with Ancestor Sampling for Probabilistic Programs
%A Jan-Willem van de Meent
%A Hongseok Yang
%A Vikash Mansinghka
%A Frank Wood
%J Computer Science
%D 2015
%I arXiv
%X Particle Markov chain Monte Carlo techniques rank among current state-of-the-art methods for probabilistic program inference. A drawback of these techniques is that they rely on importance resampling, which results in degenerate particle trajectories and a low effective sample size for variables sampled early in a program. We here develop a formalism to adapt ancestor resampling, a technique that mitigates particle degeneracy, to the probabilistic programming setting. We present empirical results that demonstrate nontrivial performance gains.
%U http://arxiv.org/abs/1501.06769v5