AISTATS 2015poster30 citations
Particle Gibbs with Ancestor Sampling for Probabilistic Programs
Jan-Willem Meent, Hongseok Yang, Vikash Mansinghka, Frank Wood
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.
BibTeX
@InProceedings{pmlr-v38-vandemeent15,
title = {{Particle Gibbs with Ancestor Sampling for Probabilistic Programs}},
author = {Meent, Jan-Willem and Yang, Hongseok and Mansinghka, Vikash and Wood, Frank},
booktitle = {Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics},
pages = {986--994},
year = {2015},
editor = {Lebanon, Guy and Vishwanathan, S. V. N.},
volume = {38},
series = {Proceedings of Machine Learning Research},
address = {San Diego, California, USA},
month = {09--12 May},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v38/vandemeent15.pdf},
url = {https://proceedings.mlr.press/v38/vandemeent15.html},
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.}
}