Efficient Search-Based Weighted Model Integration

TL;DR

This paper introduces an efficient search-based algorithm for weighted model integration that leverages tree-structured independence to significantly improve computational performance in probabilistic inference tasks.

Contribution

It presents a novel search-based method that exploits tree primal graph structures and context-specific independence to enhance WMI efficiency.

Findings

Achieves dramatic speedups over existing WMI solvers on tree-structured problems.

Exploits sparsity and independence to reduce computational complexity.

Demonstrates effectiveness through experimental evaluations.

Abstract

Weighted model integration (WMI) extends Weighted model counting (WMC) to the integration of functions over mixed discrete-continuous domains. It has shown tremendous promise for solving inference problems in graphical models and probabilistic programming. Yet, state-of-the-art tools for WMI are limited in terms of performance and ignore the independence structure that is crucial to improving efficiency. To address this limitation, we propose an efficient model integration algorithm for theories with tree primal graphs. We exploit the sparse graph structure by using search to performing integration. Our algorithm greatly improves the computational efficiency on such problems and exploits context-specific independence between variables. Experimental results show dramatic speedups compared to existing WMI solvers on problems with tree-shaped dependencies.