# Structural Refinement of Bayesian Networks for Efficient Model Parameterisation

arXiv: https://arxiv.org/abs/2510.00334 · HTML: https://mlsanity.com/paper/2510.00334/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 69 references that are in the map.
- First posted on arXiv 30 Sep 2025.

## Builds on (most cited first, 9 of 9)

- [European Union regulations on algorithmic decision-making and a "right to explanation"](https://mlsanity.com/paper/1606.08813/) (2016)
- [A survey of Bayesian Network structure learning](https://mlsanity.com/paper/2109.11415/) (2021)
- [A Comprehensive Scoping Review of Bayesian Networks in Healthcare: Past, Present and Future](https://mlsanity.com/paper/2002.08627/) (2020)
- [bnRep: A repository of Bayesian networks from the academic literature](https://mlsanity.com/paper/2409.19158/) (2024)
- [Causal Independence for Knowledge Acquisition and Inference](https://mlsanity.com/paper/1303.1468/) (2013)
- [Structure and Parameter Learning for Causal Independence and Causal Interaction Models](https://mlsanity.com/paper/1302.1561/) (2013)
- [Pruning Bayesian Networks for Efficient Computation](https://mlsanity.com/paper/1304.1112/) (2013)
- [Dynamic Bayesian Networks, Elicitation and Data Embedding for Secure Environments](https://mlsanity.com/paper/2409.07389/) (2024)
- [Surjective Independence of Causal Influences for Local Bayesian Network Structures](https://mlsanity.com/paper/2509.24759/) (2025)

## Abstract (arXiv)

Many Bayesian network modelling applications suffer from the issue of data scarcity. Hence the use of expert judgement often becomes necessary to determine the parameters of the conditional probability tables (CPTs) throughout the network. There are usually a prohibitively large number of these parameters to determine, even when complementing any available data with expert judgements. To address this challenge, a number of CPT approximation methods have been developed that reduce the quantity and complexity of parameters needing to be determined to fully parameterise a Bayesian network. This paper provides a review of a variety of structural refinement methods that can be used in practice to efficiently approximate a CPT within a Bayesian network. We not only introduce and discuss the intrinsic properties and requirements of each method, but we evaluate each method through a worked example on a Bayesian network model of cardiovascular risk assessment. We conclude with practical guidance to help Bayesian network practitioners choose an alternative approach when direct parameterisation of a CPT is infeasible.