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Maximum likelihood / Fisher information / Bayesian network / Minimum description length / Likelihood function / Bayesian information criterion / Naive Bayes classifier / Markov chain / Statistics / Estimation theory / Bayesian statistics


Bayesian Network Structure Learning using Factorized NML Universal Models Teemu Roos, Tomi Silander, Petri Kontkanen, and Petri Myllymäki Complex Systems Computation Group, Helsinki Institute for Information Technology
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Document Date: 2008-01-05 04:28:58

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City

San Mateo / Sendai / Lisbon / San Diego / /

Company

Oxford University Press / MIT Press / X5 / EURASIP Journal / /

Country

Japan / Portugal / /

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Facility

University of Helsinki / Helsinki Institute / Helsinki University of Technology P.O.Box / Information Technology HIIT University of Helsinki / /

IndustryTerm

last-step minimax algorithm / smallest networks / distributive law / approximate sum-product algorithm / generalized distributive law / sum-product algorithm / linear-time algorithm / computation algorithms / naive solution / /

Organization

Royal Statistical Society / vol. / Department of Computer Science / MIT / University of Helsinki / Oxford University / U.S. Securities and Exchange Commission / University of Technology P.O.Box / Finnish Funding Agency for Technology and Innovation / Helsinki Institute / Academy of Finland / Helsinki University of Technology / /

Person

Liang / Laplace / Barron / Paj / Morgan Kaufmann / Tomi Silander / /

Position

Bernoulli model / universal model for Bayesian networks / Fisher information approximation / Fisher / usual Fisher information approximation / /

ProgrammingLanguage

PASCAL / ML / /

ProvinceOrState

California / /

PublishedMedium

Journal of the Royal Statistical Society / IEEE Transactions on Information Theory / Machine Learning / /

Technology

approximate sum-product algorithm / Bioinformatics / sum-product algorithm / Information Technology / last-step minimax algorithm / NML computation algorithms / Machine Learning / linear-time algorithm / /

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