# Context-Specific Likelihood Weighting

arXiv: https://arxiv.org/abs/2101.09791 · HTML: https://mlsanity.com/paper/2101.09791/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 31 references that are in the map.
- First posted on arXiv 24 Jan 2021.

## Builds on (most cited first, 10 of 10)

- [A Differential Approach to Inference in Bayesian Networks](https://mlsanity.com/paper/1301.3847/) (2013)
- [Empirical Analysis of Predictive Algorithms for Collaborative Filtering](https://mlsanity.com/paper/1301.7363/) (2013)
- [Context-Specific Independence in Bayesian Networks](https://mlsanity.com/paper/1302.3562/) (2013)
- [The Bayesian Structural EM Algorithm](https://mlsanity.com/paper/1301.7373/) (2013)
- [A Bayesian Approach to Learning Bayesian Networks with Local Structure](https://mlsanity.com/paper/1302.1528/) (2013)
- [Efficient Search-Based Weighted Model Integration](https://mlsanity.com/paper/1903.05334/) (2019)
- [Approximate Knowledge Compilation by Online Collapsed Importance Sampling](https://mlsanity.com/paper/1805.12565/) (2018)
- [Weighing and Integrating Evidence for Stochastic Simulation in Bayesian Networks](https://mlsanity.com/paper/1304.1504/) (2013)
- [Context-Specific Approximation in Probabilistic Inference](https://mlsanity.com/paper/1301.7408/) (2013)
- [Simulation Approaches to General Probabilistic Inference on Belief Networks](https://mlsanity.com/paper/1304.1526/) (2013)

## Built on by (most cited first, 1 of 1)

- [First-Order Context-Specific Likelihood Weighting in Hybrid Probabilistic Logic Programs](https://mlsanity.com/paper/2201.11165/) (2022)

## Abstract (arXiv)

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.