# An Empirical Analysis of Likelihood-Weighting Simulation on a Large, Multiply-Connected Belief Network

arXiv: https://arxiv.org/abs/1304.1141 · HTML: https://mlsanity.com/paper/1304.1141/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2013.

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

- [Unifying AI Algorithms with Probabilistic Programming using Implicitly Defined Representations](https://mlsanity.com/paper/2110.02325/) (2021)

## Abstract (arXiv)

We analyzed the convergence properties of likelihood- weighting algorithms on a two-level, multiply connected, belief-network representation of the QMR knowledge base of internal medicine. Specifically, on two difficult diagnostic cases, we examined the effects of Markov blanket scoring, importance sampling, demonstrating that the Markov blanket scoring and self-importance sampling significantly improve the convergence of the simulation on our model.