Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs

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.

• Type: Journal
• Year: 2015
• Where: PKDD
• Authors: David Tolpin, Jan-Willem van de Meent, Brooks Paige, Frank Wood
• Keywords: Data Mining, PKDD 2015