NDLI: Particle methods for maximum likelihood estimation in latent variable models

Content Provider	Springer Nature Link
Author	Johansen, Adam M. Doucet, Arnaud Davy, Manuel
Copyright Year	2007
Abstract	Standard methods for maximum likelihood parameter estimation in latent variable models rely on the Expectation-Maximization algorithm and its Monte Carlo variants. Our approach is different and motivated by similar considerations to simulated annealing; that is we build a sequence of artificial distributions whose support concentrates itself on the set of maximum likelihood estimates. We sample from these distributions using a sequential Monte Carlo approach. We demonstrate state-of-the-art performance for several applications of the proposed approach.
Starting Page	47
Ending Page	57
Page Count	11
File Format	PDF
ISSN	09603174
Journal	Statistics and Computing
Volume Number	18
Issue Number	1
e-ISSN	15731375
Language	English
Publisher	Springer US
Publisher Date	2007-09-28
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Latent variable models Markov chain Monte Carlo Maximum likelihood Sequential Monte Carlo Simulated annealing Artificial Intelligence (incl. Robotics) Mathematics Numeric Computing Statistics Statistics and Computing/Statistics Programs
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Theoretical Computer Science Computational Theory and Mathematics Statistics, Probability and Uncertainty

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Particle methods for maximum likelihood estimation in latent variable models