一句话：提出随机惰性知识编译，使离散概率程序的精确推断可惰性加速，并用序列蒙特卡洛实现近似推断。
本文研究离散概率程序中的精确与近似推断，提出两种利用惰性求值的新技术。首先，将知识编译——一种离散概率程序精确推断的最先进方法——变为惰性，从而获得渐近加速。其次，概率程序的惰性语义自然将其随机选择划分为若干子问题；这些子问题可由序列蒙特卡洛依次求解，其局部最优提议分布则通过惰性知识编译自动计算。作者在工具 Pluck 中实现该方法，并在推断基准上与当前最先进方法比较。实验表明，惰性知识编译常快于现有方法，有时快数个数量级。
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 locallyoptimal 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 !nd that on a suite of inference benchmarks, lazy knowledge compilation can be faster than state-of-the-art approaches, sometimes by orders of magnitude.
@article{paperbot3143,
title = {Stochastic Lazy Knowledge Compilation for Inference in Discrete Probabilistic Programs},
author = {Maddy Bowers and Alexander K. Lew and Joshua B. Tenenbaum and Armando Solar-Lezama and Vikash K. Mansinghka},
journal = {Proceedings of the ACM on Programming Languages},
volume = {9},
number = {PLDI},
year = {2025},
doi = {10.1145/3729325}
}