Monotonicity in Bayesian Networks for Computerized Adaptive Testing

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F17%3A00313230" target="_blank" >RIV/68407700:21340/17:00313230 - isvavai.cz</a>

Alternative codes found

RIV/67985556:_____/17:00476602

Result on the web

<a href="https://link.springer.com/chapter/10.1007/978-3-319-61581-3_12" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-319-61581-3_12</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-61581-3_12" target="_blank" >10.1007/978-3-319-61581-3_12</a>

Result language

angličtina

Original language name

Monotonicity in Bayesian Networks for Computerized Adaptive Testing

Original language description

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.

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/GA16-12010S" target="_blank" >GA16-12010S: Conditional independence structures: combinatorial and optimization methods</a><br>

Continuities

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2017

Confidentiality

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

Article name in the collection

Symbolic and Quantitative Approaches to Reasoning with Uncertainty

ISBN

978-3-319-61580-6

ISSN

0302-9743

e-ISSN

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Number of pages

10

Pages from-to

125-134

Publisher name

Springer, Cham

Place of publication

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Event location

Lugano

Event date

Jul 10, 2017

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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