Structural learning of mixed noisy-OR Bayesian networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F23%3A00581903" target="_blank" >RIV/67985556:_____/23:00581903 - isvavai.cz</a>

Alternative codes found

RIV/61989592:15210/23:73620687

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0888613X23001214?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0888613X23001214?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ijar.2023.108990" target="_blank" >10.1016/j.ijar.2023.108990</a>

Result language

angličtina

Original language name

Structural learning of mixed noisy-OR Bayesian networks

Original language description

In this paper we discuss learning Bayesian networks whose conditional probability tables are either Noisy-OR models or general conditional probability tables. We refer to these models as Mixed Noisy-OR Bayesian Networks. To learn their structure, we modify the Bayesian Information Criterion used for standard Bayesian networks to reflect the number of parameters of a Noisy-OR model. We prove that the log-likelihood function of a Noisy-OR model has a unique maximum and adapt the EM-learning method for the leaky Noisy-OR model. We propose a structure learning algorithm that learns optimal Mixed Noisy-OR Bayesian Networks. We evaluate the proposed approach on synthetic data, where it performs substantially better than standard Bayesian networks. We perform experiments with Bipartite Noisy-OR Bayesian networks of different complexity to find out when the results of Mixed Noisy-OR Bayesian Networks are significantly better than the results of standard Bayesian networks and when they perform similarly. We also study how different penalties based on the number of model parameters affect the quality of the results. Finally, we apply the suggested approach to a problem from the domain of linguistics. Specifically, we use Mixed Noisy-OR Bayesian Networks to model the spread of loanwords in the South-East Asian Archipelago. We perform numerical experiments in which we compare the prediction ability of standard Bayesian networks with Mixed Noisy-OR Bayesian networks and test different pruning methods to reduce the number of parent sets considered.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20202 - Communication engineering and systems

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

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

International Journal of Approximate Reasoning

ISSN

0888-613X

e-ISSN

1873-4731

Volume of the periodical

161

Issue of the periodical within the volume

1

Country of publishing house

US - UNITED STATES

Number of pages

18

Pages from-to

108990

UT code for WoS article

001064515700001

EID of the result in the Scopus database

2-s2.0-85166914975