A New Approach to Probabilistic Programming Inference
This work addresses inference challenges in probabilistic programming for researchers and practitioners, though it appears incremental as it builds on existing methods.
The authors tackled the problem of inference in expressive probabilistic programming languages by introducing a particle Markov chain Monte Carlo approach, which they demonstrated to be more efficient than single-site Metropolis-Hastings methods in experiments.
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.