Particle Gibbs with Ancestor Sampling for Probabilistic Programs

Conference item

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.

Authors: van de Meent, J; Yang, H; Mansinghka, V; Wood, F

• Publication status: Published
• Peer review status: Peer reviewed
• Edition: Publisher's version
• DOI: http://jmlr.org/proceedings/papers/v38/vandemeent15.pdf
• Language: English