Context-Specific Likelihood Weighting
This work addresses a bottleneck in probabilistic inference for AI and machine learning applications, offering an incremental improvement over existing sampling methods.
The paper tackles the problem of inefficient sampling in approximate inference by introducing Context-Specific Likelihood Weighting (CS-LW), which exploits context-specific independence properties to reduce sampling variance and speed up convergence, showing competitive performance with state-of-the-art algorithms when significant CSIs are present.
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.