bibtype	C - Conference Paper (international conference)
ARLID	0639050
utime	20251006140507.3
mtime	20250918235959.9
title (primary) (eng)	Structural Learning of BN2A models
specification	page_count 12 s. media_type P
serial	ARLID cav_un_epca*0636591 ISBN 978-80-7378-525-3 title Proceedings of the 13th Workshop on Uncertainty Processing (WUPES’25) page_num 176-187 publisher place Prague name MatfyzPress year 2025 editor name1 Studený name2 Milan editor name1 Ay name2 Nihat editor name1 Capotorti name2 Andrea editor name1 Csirmaz name2 László editor name1 Jiroušek name2 Radim editor name1 Kleiter name2 Gernot D. editor name1 Shenoy name2 Prakash P.
keyword	Bayesian networks
keyword	Psychometrical models
keyword	Noisy-AND models
author (primary)	ARLID cav_un_auth*0458433 name1 Pérez Cabrera name2 Iván institution UTIA-B full_dept (cz) Zpracování signálů full_dept (eng) Department of Signal Processing department (cz) ZS department (eng) ZS country MX fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author	ARLID cav_un_auth*0101228 name1 Vomlel name2 Jiří institution UTIA-B full_dept (cz) Matematická teorie rozhodování full_dept Department of Decision Making Theory department (cz) MTR department MTR full_dept Department of Decision Making Theory fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source	url https://library.utia.cas.cz/separaty/2025/MTR/vomlel-0639050.pdf
cas_special	abstract (eng) Bayesian networks are a popular framework for modeling probabilistic relationships between random variables and have been used successfully in educational tests. There is interest in a particular type of Bayesian networks we have called BN2A, which are characterized as bipartite networks, where the first layer consists of hidden variables (which commonly represent skills) and the second layer consists of observed variables (which represent questions in a test). In BN2A models all variables are assumed to be binary. The variables in the second layer depend on the variables in the first layer and this dependence is characterized by conditional probability tables representing Noisy-AND models. In this work, we propose an Expectation-Maximization (EM) algorithm for learning the structure of BN2A models, that is, for learning the relationship between hidden variables and observed variables. To test the structural learning algorithm, we conducted two experiments. For the first experiment, we used synthetic data generated from a BN2A model that we previously defined, while for the second experiment we used a well-known real-world dataset in the field of Cognitive Diagnostic Models, the Fraction Subtraction dataset. Our proposed algorithm has interesting potential use cases. One key application is to generate a reasonably accurate BN2A structure model for educational diagnosis, particularly in scenarios where no prior model exists. Depending on the required level of accuracy, the estimated model can be used directly to analyze skill profiles or serve as an initial framework for test designers, who can further refine it before implementation. action ARLID cav_un_auth*0493361 name Workshop on Uncertainty Processing (WUPES’25) dates 20250604 mrcbC20-s 20250607 place Třešť country CZ RIV JD FORD0 10000 FORD1 10200 FORD2 10201 reportyear 2026 num_of_auth 2 presentation_type PR inst_support RVO:67985556 permalink https://hdl.handle.net/11104/0370050 cooperation ARLID cav_un_auth*0363021 name Faculty of Mathematics and Physics, Charles University Prague, Sokolovská 83,186 75 Praha, Czech Republic country CZ confidential S arlyear 2025 mrcbU14 SCOPUS mrcbU24 PUBMED mrcbU34 WOS mrcbU63 cav_un_epca*0636591 Proceedings of the 13th Workshop on Uncertainty Processing (WUPES’25) 978-80-7378-525-3 176 187 Prague MatfyzPress 2025 719 mrcbU67 Studený Milan 340 mrcbU67 Ay Nihat 340 mrcbU67 Capotorti Andrea 340 mrcbU67 Csirmaz László 340 mrcbU67 Jiroušek Radim 340 mrcbU67 Kleiter Gernot D. 340 mrcbU67 Shenoy Prakash P. 340