Stochastic Lazy Knowledge Compilation for Inference in Discrete Probabilistic Programs

Abstract

We present new techniques for exact and approximate inference in discrete probabilistic programs, based on two new ways of exploiting lazy evaluation. First, we show how knowledge compilation, a state-of-the art technique for exact inference in discrete probabilistic programs, can be made lazy, enabling asymptotic speed-ups. Second, we show how a probabilistic program’s lazy semantics naturally give rise to a division of its random choices into subproblems, which can be solved in sequence by sequential Monte Carlo with locally-optimal proposals automatically computed via lazy knowledge compilation. We implement our approach in a new tool, Pluck, and evaluate its performance against state-of-the-art approaches to inference in discrete probabilistic languages. We find that on a suite of inference benchmarks, lazy knowledge compilation can be faster than state-of-the-art approaches, sometimes by orders of magnitude.