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Parallell interacting MCMC for learning of topologies of graphical models
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.ORCID iD: 0000-0003-1489-8512
2008 (English)In: Data mining and knowledge discovery, ISSN 1384-5810, E-ISSN 1573-756X, Vol. 17, no 3, 431-456 p.Article in journal (Refereed) Published
Abstract [en]

Automated statistical learning of graphical models from data has attained a considerable degree of interest in the machine learning and related literature. Many authors have discussed and/or demonstrated the need for consistent stochastic search methods that would not be as prone to yield locally optimal model structures as simple greedy methods. However, at the same time most of the stochastic search methods are based on a standard Metropolis-Hastings theory that necessitates the use of relatively simple random proposals and prevents the utilization of intelligent and efficient search operators. Here we derive an algorithm for learning topologies of graphical models from samples of a finite set of discrete variables by utilizing and further enhancing a recently introduced theory for non-reversible parallel interacting Markov chain Monte Carlo-style computation. In particular, we illustrate how the non-reversible approach allows for novel type of creativity in the design of search operators. Also, the parallel aspect of our method illustrates well the advantages of the adaptive nature of search operators to avoid trapping states in the vicinity of locally optimal network topologies.

Place, publisher, year, edition, pages
2008. Vol. 17, no 3, 431-456 p.
Keyword [en]
MCMC, Equivalence search, Learning graphical models, chain monte-carlo, markov equivalence classes, efficient estimation, bayesian networks, acyclic digraphs, selection
URN: urn:nbn:se:kth:diva-17890DOI: 10.1007/s10618-008-0099-9ISI: 000260065200004ScopusID: 2-s2.0-54249168561OAI: diva2:335935
QC 20100525Available from: 2010-08-05 Created: 2010-08-05Bibliographically approved

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