0561324 20240111141109.320220920235959.9 12 s. E cav_un_epca*0561323Proceedings of Machine Learning Research, Volume 186 : Proceedings of The 11th International Conference on Probabilistic Graphical Models277-288Almerı́aPMLR2022SalmerónAntonioRumíRafael Bayesian networks Learning Bayesian networks Noisy-or model Applications of Bayesian networks Linguistics Loanwords cav_un_auth*0414315 Kratochvíl F. CZ cav_un_auth*0216188 Kratochvíl Václav UTIA-B Matematická teorie rozhodování Department of Decision Making Theory MTR MTR Department of Decision Making Theory CZ 33 Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101228 Vomlel Jiří UTIA-B Matematická teorie rozhodování Department of Decision Making Theory MTR MTR Department of Decision Making Theory 33 K Ústav teorie informace a automatizace AV ČR, v. v. i. online http://library.utia.cas.cz/separaty/2022/MTR/kratochvil-0561324.pdf GA20-18407S GA ČR cav_un_auth*0397557 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. cav_un_auth*0436551 International Conference on Probabilistic Graphical Models 20221005 20221007 Almería ES JD 10000 10200 10201 2023 3 PR RVO:67985556 https://hdl.handle.net/11104/0334054 cav_un_auth*0320502 Univerzita Paleckého v Olomouci, Filozofická fakulta UPOL CZ S 2022 SCOPUS PUBMED WOS online cav_un_epca*0561323 Proceedings of Machine Learning Research, Volume 186 : Proceedings of The 11th International Conference on Probabilistic Graphical Models PMLR 2022 Almerı́a 277 288 2640-3498 Salmerón Antonio 340 Rumí Rafael 340