140 citations · 735 across the 46 of their papers we have counts for
3 papers · 2 filters
A New Approach to Probabilistic Programming Inference
Frank Wood, Jan Willem van de Meent, Vikash Mansinghka
We introduce and demonstrate a new approach to inference in expressive probabilistic programming languages based on particle Markov chain Monte Carlo. Our approach is simple to imp…
Black-Box Policy Search with Probabilistic Programs
Jan-Willem van de Meent, Brooks Paige, David Tolpin +1
In this work, we explore how probabilistic programs can be used to represent policies in sequential decision problems. In this formulation, a probabilistic program is a black-box s…
Particle Gibbs with Ancestor Sampling for Probabilistic Programs
Jan-Willem van de Meent, Hongseok Yang, Vikash Mansinghka +1
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 im…