| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0599042 |
| utime |
20250317085438.7 |
| mtime |
20241007235959.9 |
| SCOPUS |
85216632654 |
| WOS |
001347210900023 |
| title
(primary) (eng) |
Enhancing Bayesian Networks with Psychometric Models |
| specification |
| page_count |
14 s. |
| media_type |
E |
|
| serial |
| ARLID |
cav_un_epca*0599192 |
| ISSN |
Proceedings of Machine Learning Research (PMLR), Volume 246 : International Conference on Probabilistic Graphical Models |
| title
|
Proceedings of Machine Learning Research (PMLR), Volume 246 : International Conference on Probabilistic Graphical Models |
| page_num |
401-414 |
| publisher |
| place |
San Diego |
| name |
JMLR-JOURNAL MACHINE LEARNING RESEARCH |
| year |
2024 |
|
|
| keyword |
Bayesian networks |
| keyword |
Parameter Learning |
| keyword |
Hidden Variables |
| keyword |
BN2A models |
| keyword |
Cognitive Diagnostic Modeling |
| keyword |
Psychometrics |
| 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 |
|
| source |
|
| cas_special |
| project |
| project_id |
GA22-11101S |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0435406 |
|
| abstract
(eng) |
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. |
| action |
| ARLID |
cav_un_auth*0473000 |
| name |
International Conference on Probabilistic Graphical Models 2024 /12./ |
| dates |
20240911 |
| mrcbC20-s |
20240913 |
| place |
Nijmegen |
| country |
NL |
|
| RIV |
JD |
| FORD0 |
10000 |
| FORD1 |
10200 |
| FORD2 |
10201 |
| reportyear |
2025 |
| num_of_auth |
2 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| permalink |
https://hdl.handle.net/11104/0356725 |
| confidential |
S |
| arlyear |
2024 |
| mrcbU14 |
85216632654 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
001347210900023 WOS |
| mrcbU63 |
cav_un_epca*0599192 Proceedings of Machine Learning Research (PMLR), Volume 246 : International Conference on Probabilistic Graphical Models JMLR-JOURNAL MACHINE LEARNING RESEARCH 2024 San Diego 401 414 2640-3498 |
|