Finding an Ε-Close Minimal Variation of Parameters in Bayesian Networks
Abstract
This paper addresses the ε-close parameter tuning problem for Bayesian networks (BNs): find a minimal ε-close amendment of probability entries in a given set of (rows in) conditional probability tables that make a given quantitative constraint on the BN valid. Based on the state-of-the-art “region verification” techniques for parametric Markov chains, we propose an algorithm whose capabilities go beyond any existing techniques. Our experiments show that ε-close tuning of large BN benchmarks with up to eight parameters is feasible. In particular, by allowing (i) varied parameters in multiple CPTs and (ii) inter-CPT parameter dependencies, we treat subclasses of parametric BNs that have received scant attention so far.
Cite
Text
Salmani and Katoen. "Finding an Ε-Close Minimal Variation of Parameters in Bayesian Networks." International Joint Conference on Artificial Intelligence, 2023. doi:10.24963/IJCAI.2023/635Markdown
[Salmani and Katoen. "Finding an Ε-Close Minimal Variation of Parameters in Bayesian Networks." International Joint Conference on Artificial Intelligence, 2023.](https://mlanthology.org/ijcai/2023/salmani2023ijcai-finding/) doi:10.24963/IJCAI.2023/635BibTeX
@inproceedings{salmani2023ijcai-finding,
title = {{Finding an Ε-Close Minimal Variation of Parameters in Bayesian Networks}},
author = {Salmani, Bahare and Katoen, Joost-Pieter},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2023},
pages = {5720-5729},
doi = {10.24963/IJCAI.2023/635},
url = {https://mlanthology.org/ijcai/2023/salmani2023ijcai-finding/}
}