Context-Specific Likelihood Weighting

TL;DR

This paper introduces context-specific likelihood weighting (CS-LW), a sampling method that exploits context-specific independence to improve efficiency and reduce variance in approximate inference.

Contribution

The paper proposes a novel sampling algorithm, CS-LW, that leverages context-specific independence, reducing sample requirements and increasing sampling speed compared to standard methods.

Findings

CS-LW reduces the number of samples needed for convergence.

CS-LW increases the speed of sample generation.

Empirical results show CS-LW is competitive with state-of-the-art algorithms.

Abstract

Sampling is a popular method for approximate inference when exact inference is impractical. Generally, sampling algorithms do not exploit context-specific independence (CSI) properties of probability distributions. We introduce context-specific likelihood weighting (CS-LW), a new sampling methodology, which besides exploiting the classical conditional independence properties, also exploits CSI properties. Unlike the standard likelihood weighting, CS-LW is based on partial assignments of random variables and requires fewer samples for convergence due to the sampling variance reduction. Furthermore, the speed of generating samples increases. Our novel notion of contextual assignments theoretically justifies CS-LW. We empirically show that CS-LW is competitive with state-of-the-art algorithms for approximate inference in the presence of a significant amount of CSIs.