Some aspects of latent structure analysis

Titterington, M. (2006) Some aspects of latent structure analysis. Lecture Notes in Computer Science(3940), pp. 69-83. (doi:10.1007/11752790_4)

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Abstract

Latent structure models involve real, potentially observable variables and latent, unobservable variables. The framework includes various particular types of model, such as factor analysis, latent class analysis, latent trait analysis, latent profile models, mixtures of factor analysers, state-space models and others. The simplest scenario, of a single discrete latent variable, includes finite mixture models, hidden Markov chain models and hidden Markov random field models. The paper gives a brief tutorial of the application of maximum likelihood and Bayesian approaches to the estimation of parameters within these models, emphasising especially the fact that computational complexity varies greatly among the different scenarios. In the case of a single discrete latent variable, the issue of assessing its cardinality is discussed. Techniques such as the EM algorithm, Markov chain Monte Carlo methods and variational approximations are mentioned.

Item Type:Articles
Additional Information:The original publication is available at www.springerlink.com
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Titterington, Professor Michael
Authors: Titterington, M.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Lecture Notes in Computer Science
Publisher:Springer
ISSN:0302-9743
ISSN (Online):1611-3349
ISBN:9783540341383
Published Online:24 May 2006
Copyright Holders:Copyright © 2006 Springer
First Published:First published in Lecture Notes in Computer Science 3940:69-83
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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