Learning Noisy-Or Networks with an Application in Linguistics

0561324 - ÚTIA 2023 RIV ES eng C - Conference Paper (international conference)
Kratochvíl, F. - Kratochvíl, Václav - Vomlel, Jiří
Learning Noisy-Or Networks with an Application in Linguistics.
Proceedings of Machine Learning Research, Volume 186 : Proceedings of The 11th International Conference on Probabilistic Graphical Models. Almerı́a: PMLR, 2022 - (Salmerón, A.; Rumí, R.), s. 277-288. E-ISSN 2640-3498.
[International Conference on Probabilistic Graphical Models. Almería (ES), 05.10.2022-07.10.2022]
R&D Projects: GA ČR GA20-18407S
Institutional support: RVO:67985556
Keywords : Bayesian networks * Learning Bayesian networks * Noisy-or model * Applications of Bayesian networks * Linguistics * Loanwords
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://library.utia.cas.cz/separaty/2022/MTR/kratochvil-0561324.pdf

In this paper we discuss the issue of learning Bayesian networks whose conditional probability tables (CPTs) are either noisy-or models or general CPTs. We refer to these models as Mixed Noisy-Or Bayesian Networks. In order to learn the structure of such Bayesian networks we modify the Bayesian Information Criteria (BIC) used for general Bayesian networks so that it reflects the number of parameters of a noisy-or model. We prove the log-likelihood function of a noisy-or model has a unique maximum and adapt the EM-learning method for leaky noisy-or models. We evaluate the proposed approach on synthetic data where it performs substantially better than general BNs. We apply this approach also to a problem from the domain of linguistics. We use Mixed Noisy-Or Bayesian Networks to model spread of loanwords in the South-East Asia Archipelago. We perform numerical experiments in which we compare prediction ability of general Bayesian Networks with Mixed Noisy-Or Bayesian Networks.
Permanent Link: https://hdl.handle.net/11104/0334054