Particle Gibbs with Ancestor Sampling for Probabilistic Programs

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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Text

van de Meent et al. "Particle Gibbs with Ancestor Sampling for Probabilistic Programs." International Conference on Artificial Intelligence and Statistics, 2015.

Markdown

[van de Meent et al. "Particle Gibbs with Ancestor Sampling for Probabilistic Programs." International Conference on Artificial Intelligence and Statistics, 2015.](https://mlanthology.org/aistats/2015/vandemeent2015aistats-particle/)

BibTeX

@inproceedings{vandemeent2015aistats-particle,
  title     = {{Particle Gibbs with Ancestor Sampling for Probabilistic Programs}},
  author    = {van de Meent, Jan-Willem and Yang, Hongseok and Mansinghka, Vikash and Wood, Frank D.},
  booktitle = {International Conference on Artificial Intelligence and Statistics},
  year      = {2015},
  url       = {https://mlanthology.org/aistats/2015/vandemeent2015aistats-particle/}
}