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OverviewTheory and Use of the EM Algorithm introduces the expectation-maximization (EM) algorithm and provides an intuitive and mathematically rigorous understanding of this method. It describes in detail two of the most popular applications of EM: estimating Gaussian mixture models (GMMs), and estimating hidden Markov models (HMMs). It also covers the use of EM for learning an optimal mixture of fixed models, for estimating the parameters of a compound Dirichlet distribution, and for disentangling superimposed signals. It discusses problems that arise in practice with EM, and variants of the algorithm that help deal with these challenges. Theory and Use of the EM Algorithm is designed to be useful to both the EM novice and the experienced EM user looking to better understand the method and its use. Full Product DetailsAuthor: Maya R. Gupta , Yihua ChenPublisher: now publishers Inc Imprint: now publishers Inc Volume: 11 Dimensions: Width: 15.60cm , Height: 0.50cm , Length: 23.40cm Weight: 0.137kg ISBN: 9781601984302ISBN 10: 1601984308 Pages: 88 Publication Date: 30 June 2011 Audience: Professional and scholarly , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: Out of print, replaced by POD  We will order this item for you from a manufatured on demand supplier. Table of Contents1: The Expectation-Maximization Method 2: Analysis of EM 3: Learning Mixtures 4: More EM Examples 5: EM Variants 6: Conclusions and Some Historical Notes. Acknowledgements. References.ReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |
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