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%2F67985556%3A_____%2F22%3A00561324" target="_blank" >RIV/67985556:_____/22:00561324 - isvavai.cz</a>

Alternative codes found

RIV/61989592:15210/22:73618985

Result on the web

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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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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA20-18407S" target="_blank" >GA20-18407S: Verb Class Analysis Accelerator for Low-Resource Languages - RoboCorp</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

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 : Proceedings of The 11th International Conference on Probabilistic Graphical Models

ISBN

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ISSN

2640-3498

e-ISSN

2640-3498

Number of pages

12

Pages from-to

277-288

Publisher name

PMLR

Place of publication

Almerı́a

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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