Particle Gibbs with Ancestor Sampling for Probabilistic Programs

Jan-Willem van de Meent; Hongseok Yang; Vikash Mansinghka; Frank Wood

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arXiv · 1501.06769

Particle Gibbs with Ancestor Sampling for Probabilistic Programs

Jan-Willem van de Meent·Hongseok Yang·Vikash Mansinghka·Frank Wood

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Abstract

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.

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Jan-Willem van de Meent, Hongseok Yang, Vikash Mansinghka, Frank Wood. 2015-02-09. Particle Gibbs with Ancestor Sampling for Probabilistic Programs. https://arxiv.org/abs/1501.06769

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