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Graphical models / Estimation theory / Markov models / Bayesian statistics / Bioinformatics / Hidden Markov model / Expectation–maximization algorithm / Particle filter / Bayesian network / Statistics / Mathematics / Probability and statistics


Dynamic Bayesian Networks: Representation, Inference and Learning by
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Document Date: 2002-09-09 15:01:07


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File Size: 1,74 MB

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Company

ABSTRACT Dynamic Bayesian Networks / HP / Compaq / IR / RUGS / Berkeley Fall 2002 Dynamic Bayesian Networks / Dynamic Bayesian Networks / Intel / /

Country

Russia / United States / China / /

Facility

Computer Science University of California / Cambridge University / University of Pennsylvania / UNIVERSITY OF CALIFORNIA / /

IndustryTerm

online inference / junction tree algorithm / interface algorithm / Online parameter estimation / stochastic algorithms / Online filtering / deterministic algorithms / turn-key algorithm / hybrid systems / frontier algorithm / approximate inference algorithm / /

Organization

Cambridge University / RUGS and IR / University of Pennsylvania / UNIVERSITY OF CALIFORNIA / BERKELEY / ICRC / BERKELEY Committee / Date Date Date Date University / GRADUATE DIVISION / /

Person

Jim Rehg / Paul Horton / Serge Belongie / Nancy Chang / Margaret / Michael Jordan Professor Peter / Mark Paskin / Michael Jordan / Sekhar Tatikonda / Jeff Bilmes / Wei Hu / Peter Bickel Professor Jeffrey Bilmes / Berkeley Professor Stuart Russell / David Andre / Geoff Zweig / Stuart Russell / Peter Bickel / Andrew Ng / Sebastian Thrun / Yimin Zhang / Gary Bradski / Kevin Patrick Murphy / Jeffrey Bilmes Fall / /

Position

advisor / Professor / Chair / /

ProvinceOrState

Pennsylvania / California / /

Technology

speech recognition / junction tree algorithm / 63 The frontier algorithm / Island algorithm / frontier algorithm / structural EM algorithm / 66 The interface algorithm / approximate inference algorithm / turn-key algorithm / artificial intelligence / interface algorithm / BK algorithm / /

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