December 10, 2014

Output-Sensitive Adaptive MH for Probabilistic Programs

Filed under: Machine Learning — dvd @ 12:20 pm

A poster for the 3rd NIPS Workshop on Probabilistic Programming; also available as A0 PDF. Slides for a 15-minute talk.


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.

Full paper.

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