# Using ROBDDs for Inference in Bayesian Networks with Troubleshooting as an Example

arXiv: https://arxiv.org/abs/1301.3880 · HTML: https://mlsanity.com/paper/1301.3880/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2013.

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

- [Reliability Analysis of Complex Multi-State System Based on Universal Generating Function and Bayesian Network](https://mlsanity.com/paper/2208.04130/) (2022)

## Abstract (arXiv)

When using Bayesian networks for modelling the behavior of man-made machinery, it usually happens that a large part of the model is deterministic. For such Bayesian networks deterministic part of the model can be represented as a Boolean function, and a central part of belief updating reduces to the task of calculating the number of satisfying configurations in a Boolean function. In this paper we explore how advances in the calculation of Boolean functions can be adopted for belief updating, in particular within the context of troubleshooting. We present experimental results indicating a substantial speed-up compared to traditional junction tree propagation.