Work

A New Approach to Probabilistic Programming Inference

Year: 2015

Type: preprint

Abstract: 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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1507.00996

Authors: Wood, Frank, van de Meent, Jan Willem, Mansinghka, Vikash

Institutions: University of Oxford, Columbia University, Massachusetts Institute of Technology

Language: English

Topic: Markov Chains and Monte Carlo Methods

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2950113781

Cites: 8

Cited by: 6

Related to: 19