# Weighing and Integrating Evidence for Stochastic Simulation in Bayesian Networks

arXiv: https://arxiv.org/abs/1304.1504 · HTML: https://mlsanity.com/paper/1304.1504/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2013.

## Built on by (most cited first, 4 of 4)

- [Declarative Probabilistic Logic Programming in Discrete-Continuous Domains](https://mlsanity.com/paper/2302.10674/) (2023)
- [Fast-PGM: Fast Probabilistic Graphical Model Learning and Inference](https://mlsanity.com/paper/2405.15605/) (2024)
- [Context-Specific Likelihood Weighting](https://mlsanity.com/paper/2101.09791/) (2021)
- [Neural Bayesian Networks for Modular and Scalable Decision-Making Under Uncertainty](https://mlsanity.com/paper/2610.16902/) (2026)

## Abstract (arXiv)

Stochastic simulation approaches perform probabilistic inference in Bayesian networks by estimating the probability of an event based on the frequency that the event occurs in a set of simulation trials. This paper describes the evidence weighting mechanism, for augmenting the logic sampling stochastic simulation algorithm [Henrion, 1986]. Evidence weighting modifies the logic sampling algorithm by weighting each simulation trial by the likelihood of a network's evidence given the sampled state node values for that trial. We also describe an enhancement to the basic algorithm which uses the evidential integration technique [Chin and Cooper, 1987]. A comparison of the basic evidence weighting mechanism with the Markov blanket algorithm [Pearl, 1987], the logic sampling algorithm, and the evidence integration algorithm is presented. The comparison is aided by analyzing the performance of the algorithms in a simple example network.