# Practical Issues in Constructing a Bayes' Belief Network

arXiv: https://arxiv.org/abs/1304.2725 · HTML: https://mlsanity.com/paper/1304.2725/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2013.

## Built on by (most cited first, 2 of 2)

- [A Causal Approach for Unfair Edge Prioritization and Discrimination Removal](https://mlsanity.com/paper/2111.14348/) (2021)
- [Surjective Independence of Causal Influences for Local Bayesian Network Structures](https://mlsanity.com/paper/2509.24759/) (2025)

## Abstract (arXiv)

Bayes belief networks and influence diagrams are tools for constructing coherent probabilistic representations of uncertain knowledge. The process of constructing such a network to represent an expert's knowledge is used to illustrate a variety of techniques which can facilitate the process of structuring and quantifying uncertain relationships. These include some generalizations of the "noisy OR gate" concept. Sensitivity analysis of generic elements of Bayes' networks provides insight into when rough probability assessments are sufficient and when greater precision may be important.