Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs
This is an incremental improvement for researchers and practitioners using probabilistic programming, as it enhances an existing method for specific models.
The paper tackled the problem of slow convergence in Metropolis-Hastings algorithms for probabilistic programs by introducing AdLMH, which adapts proposal probabilities to improve output convergence, showing consistent improvements in test problems.
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.