A New Approach to Probabilistic Programming Inference

Research paper by Frank Wood, Jan Willem van de Meent, Vikash Mansinghka

Indexed on: 09 Jul '15Published on: 09 Jul '15Published in: Statistics - Machine Learning


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 implement and easy to parallelize. It applies to Turing-complete probabilistic programming languages and supports accurate inference in models that make use of complex control flow, including stochastic recursion. It also includes primitives from Bayesian nonparametric statistics. Our experiments show that this approach can be more efficient than previously introduced single-site Metropolis-Hastings methods.