{"product_id":"the-em-algorithm-and-extensions-geoffrey-j-mclachlan-9780471201700","title":"The Em Algorithm and Extensions","description":"The only single-source----now completely updated and revised----to offer a unified treatment of the theory, methodology, and applications of the EM algorithm \u003cp\u003eComplete with updates that capture developments from the past decade, The EM Algorithm and Extensions, Second Edition successfully provides a basic understanding of the EM algorithm by describing its inception, implementation, and applicability in numerous statistical contexts. In conjunction with the fundamentals of the topic, the authors discuss convergence issues and computation of standard errors, and, in addition, unveil many parallels and connections between the EM algorithm and Markov chain Monte Carlo algorithms. Thorough discussions on the complexities and drawbacks that arise from the basic EM algorithm, such as slow convergence and lack of an in-built procedure to compute the covariance matrix of parameter estimates, are also presented.\u003c\/p\u003e \u003cp\u003eWhile the general philosophy of the First Edition has been maintained, this timely new edition has been updated, revised, and expanded to include: \u003c\/p\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eNew chapters on Monte Carlo versions of the EM algorithm and generalizations of the EM algorithm\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eNew results on convergence, including convergence of the EM algorithm in constrained parameter spaces\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eExpanded discussion of standard error computation methods, such as methods for categorical data and methods based on numerical differentiation\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eCoverage of the interval EM, which locates all stationary points in a designated region of the parameter space\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eExploration of the EM algorithm's relationship with the Gibbs sampler and other Markov chain Monte Carlo methods\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003ePlentiful pedagogical elements--chapter introductions, lists of examples, author and subject indices, computer-drawn graphics, and a related Web site\u003c\/p\u003e \u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThe EM Algorithm and Extensions, Second Edition serves as an excellent text for graduate-level statistics students and is also a comprehensive resource for theoreticians, practitioners, and researchers in the social and physical sciences who would like to extend their knowledge of the EM algorithm.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Geoffrey J. McLachlan, Thriyambakam Krishnan\u003cbr\u003e\u003cb\u003eISBN-10:\u003c\/b\u003e 0471201707\u003cbr\u003e\u003cb\u003eISBN-13:\u003c\/b\u003e 9780471201700\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Wiley-Interscience\u003cbr\u003e\u003cb\u003eLanguage:\u003c\/b\u003e English\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 03\/01\/2008\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 400\u003cbr\u003e\u003cb\u003eFormat:\u003c\/b\u003e Hardcover\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 1.54lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.30h x 6.42w x 0.99d\u003cbr\u003e\u003cbr\u003e\u003cb\u003eReview Citation(s): \u003c\/b\u003e\u003cbr\u003e\u003ci\u003eScitech Book News\u003c\/i\u003e 06\/01\/2008 pg. 32","brand":"Geoffrey J. McLachlan","offers":[{"title":"Hardcover","offer_id":43919874326783,"sku":"9780471201700","price":169.95,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0662\/2982\/9887\/products\/img_d6a483db-6b40-46ef-9565-cdcf60eb8c3d.jpg?v=1680812388","url":"https:\/\/www.whiterainbookhouse.com\/products\/the-em-algorithm-and-extensions-geoffrey-j-mclachlan-9780471201700","provider":"WR Book House","version":"1.0","type":"link"}