Enhancing Bayesian Networks with Psychometric Models

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F24%3A00599042" target="_blank" >RIV/67985556:_____/24:00599042 - isvavai.cz</a>

Alternative codes found

RIV/67985807:_____/24:00602451

Result on the web

<a href="https://proceedings.mlr.press/v246/perez24a.html" target="_blank" >https://proceedings.mlr.press/v246/perez24a.html</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Enhancing Bayesian Networks with Psychometric Models

Original language description

Bayesian networks (BNs) are a popular framework in education and other fields. In this paper, we consider two-layer BNs, where the first layer consists of hidden binary variables that are assumed to be independent of each other, and the second layer consists of observed binary variables. The variables in the second layer depend on the variables in the first layer. The dependence is characterized by conditional probability tables, which represent Noisy-AND models. We refer to this class of models as BN2A models. We found that these models are also popular in the psychometric community, where they can be found under the name of Cognitive Diagnostic Models (CDMs), which are used to classify test takers into some latent classes according to the similarity of their responses to test questions. This paper shows the relation between some BN2A models and their corresponding CDMs. In particular, we compare the performance of these models on large-scale tests conducted in the Czech Republic in 2022. The BN2A model with general conditional probability tables produced the best absolute fit. However, when we added monotonic constraints to the General model, we obtained better predictive results.

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/GA22-11101S" target="_blank" >GA22-11101S: Tensor Decomposition in Active Fault Diagnosis for Stochastic Large Scale Systems</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

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

Article name in the collection

Proceedings of Machine Learning Research (PMLR), Volume 246 : International Conference on Probabilistic Graphical Models

ISBN

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ISSN

2640-3498

e-ISSN

2640-3498

Number of pages

14

Pages from-to

401-414

Publisher name

JMLR-JOURNAL MACHINE LEARNING RESEARCH

Place of publication

San Diego

Event location

Nijmegen

Event date

Sep 11, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001347210900023