SOTAVerified

Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs

2015-01-22Code Available0· sign in to hype

David Tolpin, Jan Willem van de Meent, Brooks Paige, Frank Wood

arXivPDF

Code Available — Be the first to reproduce this paper.

Reproduce

Code

Abstract

We introduce an adaptive output-sensitive Metropolis-Hastings algorithm for probabilistic models expressed as programs, Adaptive Lightweight Metropolis-Hastings (AdLMH). The algorithm extends Lightweight Metropolis-Hastings (LMH) by adjusting the probabilities of proposing random variables for modification to improve convergence of the program output. We show that AdLMH converges to the correct equilibrium distribution and compare convergence of AdLMH to that of LMH on several test problems to highlight different aspects of the adaptation scheme. We observe consistent improvement in convergence on the test problems.

Reproductions