Learning Noisy-Or Networks with an Application in Linguistics

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15210%2F22%3A73618985" target="_blank" >RIV/61989592:15210/22:73618985 - isvavai.cz</a>
Alternative codes found
RIV/67985556:_____/22:00561324
Result on the web
<a href="https://proceedings.mlr.press/v186/kratochvil22a/kratochvil22a.pdf" target="_blank" >https://proceedings.mlr.press/v186/kratochvil22a/kratochvil22a.pdf</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Learning Noisy-Or Networks with an Application in Linguistics
Original language description
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.
Czech name
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Czech description
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Project
<a href="/en/project/GA20-18407S" target="_blank" >GA20-18407S: Verb Class Analysis Accelerator for Low-Resource Languages - RoboCorp</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of Machine Learning Research:Volume 186: International Conference on Probabilistic Graphical Models, 5-7 October 2022, Almerı́a, Spain
ISBN
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ISSN
2640-3498
e-ISSN
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Number of pages
12
Pages from-to
277-288
Publisher name
Proceedings of Machine Learning Research
Place of publication
Almería
Event location
Almería
Event date
Oct 5, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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