NDLI: A fully multivariate DEUM algorithm

Content Provider	IEEE Xplore Digital Library
Author	Shakya, S. Brownlee, A. McCall, J. Fournier, F. Owusu, G.
Copyright Year	2009
Description	Author affiliation: School of Computing, Robert Gordon University, Aberdeen, UK (Brownlee, A.; McCall, J.) \|\| ODS-Petrodata, Aberdeen, UK (Fournier, F.) \|\| Intelligent Systems Research Centre, BT Innovate, Ipswich, UK (Shakya, S.; Owusu, G.)
Abstract	Distribution Estimation Using Markov network (DEUM) algorithm is a class of estimation of distribution algorithms that uses Markov networks to model and sample the distribution. Several different versions of this algorithm have been proposed and are shown to work well in a number of different optimisation problems. One of the key similarities between all of the DEUMalgorithms proposed so far is that they all assume the interaction between variables in the problem to be pre given. In other words, they do not learn the structure of the problem and assume that it is known in advance. Therefore, they may not be classified as full estimation of distribution algorithms. This work presents a fully multivariate DEUM algorithm that can automatically learn the undirected structure of the problem, automatically find the cliques from the structure and automatically estimate a joint probability model of the Markov network. This model is then sampled using Monte Carlo samplers. The proposed DEUM algorithm can be applied to any general optimisation problem even when the structure is not known.
Starting Page	479
Ending Page	486
File Size	253437
Page Count	8
File Format	PDF
ISBN	9781424429585
DOI	10.1109/CEC.2009.4982984
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-18
Publisher Place	Norway
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Markov random fields Electronic design automation and methodology Bayesian methods Sampling methods Monte Carlo methods Parameter estimation Glass Graphical models Probabilistic logic Probability distribution
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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