bibtype	C - Conference Paper (international conference)
ARLID	0476602
utime	20240103214323.0
mtime	20170731235959.9
SCOPUS	85025114720
WOS	000432996600012
DOI	10.1007/978-3-319-61581-3_12
title (primary) (eng)	Monotonicity in Bayesian Networks for Computerized Adaptive Testing
specification	page_count 10 s. media_type P
serial	ARLID cav_un_epca*0476601 ISBN 978-3-319-61580-6 title Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2017 page_num 125-134 publisher place Cham name Springer year 2017 editor name1 Antonucci name2 A. editor name1 Cholvy name2 L. editor name1 Papini name2 O.
keyword	computerized adaptive testing
keyword	probabilistic graphical models
keyword	gradient methods
author (primary)	ARLID cav_un_auth*0329423 name1 Plajner name2 Martin institution UTIA-B full_dept (cz) Matematická teorie rozhodování full_dept (eng) Department of Decision Making Theory department (cz) MTR department (eng) MTR full_dept Department of Decision Making Theory country CZ 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 http://library.utia.cas.cz/separaty/2017/MTR/plajner-0476602.pdf
cas_special	project ARLID cav_un_auth*0332303 project_id GA16-12010S agency GA ČR country CZ abstract (eng) Artificial intelligence is present in many modern computer science applications. The question of effectively learning parameters of such models even with small data samples is still very active. It turns out that restricting conditional probabilities of a probabilistic model by monotonicity conditions might be useful in certain situations. Moreover, in some cases, the modeled reality requires these conditions to hold. In this article we focus on monotonicity conditions in Bayesian Network models. We present an algorithm for learning model parameters, which satisfy monotonicity conditions, based on gradient descent optimization. We test the proposed method on two data sets. One set is synthetic and the other is formed by real data collected for computerized adaptive testing. We compare obtained results with the isotonic regression EM method by Masegosa et al. which also learns BN model parameters satisfying monotonicity. A comparison is performed also with the standard unrestricted EM algorithm for BN learning. Obtained experimental results in our experiments clearly justify monotonicity restrictions. As a consequence of monotonicity requirements, resulting models better fit data. action ARLID cav_un_auth*0348187 name ECSQARU: European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty dates 20170710 mrcbC20-s 20170714 place Lugano country CH RIV JD FORD0 20000 FORD1 20200 FORD2 20205 reportyear 2018 num_of_auth 2 inst_support RVO:67985556 permalink http://hdl.handle.net/11104/0273648 mrcbC62 1 confidential S mrcbC83 RIV/67985556:_____/17:00476602!RIV18-AV0-67985556 191975664 Doplnění UT WOS mrcbC83 RIV/67985556:_____/17:00476602!RIV18-GA0-67985556 191965015 Doplnění UT WOS mrcbC86 n.a. Proceedings Paper Computer Science Artificial Intelligence|Logic mrcbC86 n.a. Proceedings Paper Computer Science Artificial Intelligence|Logic mrcbC86 n.a. Proceedings Paper Computer Science Artificial Intelligence|Logic arlyear 2017 mrcbU14 85025114720 SCOPUS mrcbU24 PUBMED mrcbU34 000432996600012 WOS mrcbU63 cav_un_epca*0476601 Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2017 Springer 2017 Cham 125 134 978-3-319-61580-6 Lecture Notes in Computer Science 10369 mrcbU67 340 Antonucci A. mrcbU67 340 Cholvy L. mrcbU67 340 Papini O.