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Monotonicity in Bayesian Networks for Computerized Adaptive Testing
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SYSNO ASEP 0476602 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Monotonicity in Bayesian Networks for Computerized Adaptive Testing Author(s) Plajner, Martin (UTIA-B)
Vomlel, Jiří (UTIA-B) RID, ORCIDNumber of authors 2 Source Title Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2017. - Cham : Springer, 2017 / Antonucci A. ; Cholvy L. ; Papini O. - ISBN 978-3-319-61580-6 Pages s. 125-134 Number of pages 10 s. Publication form Print - P Action ECSQARU: European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty Event date 10.07.2017 - 14.07.2017 VEvent location Lugano Country CH - Switzerland Event type WRD Language eng - English Country DE - Germany Keywords computerized adaptive testing ; probabilistic graphical models ; gradient methods Subject RIV JD - Computer Applications, Robotics OECD category Automation and control systems R&D Projects GA16-12010S GA ČR - Czech Science Foundation (CSF) Institutional support UTIA-B - RVO:67985556 UT WOS 000432996600012 EID SCOPUS 85025114720 DOI 10.1007/978-3-319-61581-3_12 Annotation 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. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2018
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