{"product_id":"9780470748268","title":"Advanced Markov Chain Monte Carlo Methods Learning from Past Samples","description":"\u003ch3\u003eWiley Series in Computational Statistics\u003c\/h3\u003e\u003ch1\u003eAdvanced Markov Chain Monte Carlo Methods\u003c\/h1\u003e\u003ch2\u003eLearning from Past Samples\u003c\/h2\u003e\u003ch3\u003eFaming Liang | Chuanhai Liu | Raymond Carroll\u003c\/h3\u003e\u003cdiv\u003e\u003cb\u003eMathematics \/ Probability \u0026amp; Statistics \/ General\u003c\/b\u003e\u003c\/div\u003e\u003cbr\u003e\u003cdiv\u003eMarkov Chain Monte Carlo (MCMC) methods are now an indispensable tool in scientific computing. This book discusses recent developments of MCMC methods with an emphasis on those making use of past sample information during simulations. The application examples are drawn from diverse fields such as bioinformatics, machine learning, social science, combinatorial optimization, and computational physics.  \u003cp\u003e\u003cb\u003eKey Features:\u003c\/b\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eExpanded coverage of the stochastic approximation Monte Carlo and dynamic weighting algorithms that are essentially immune to local trap problems.\u003c\/li\u003e \u003cli\u003eA detailed discussion of the Monte Carlo Metropolis-Hastings algorithm that can be used for sampling from distributions with intractable normalizing constants.\u003c\/li\u003e \u003cli\u003eUp-to-date accounts of recent developments of the Gibbs sampler.\u003c\/li\u003e \u003cli\u003eComprehensive overviews of the population-based MCMC algorithms and the MCMC algorithms with adaptive proposals.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThis book can be used as a textbook or a reference book for a one-semester graduate course in statistics, computational biology, engineering, and computer sciences. Applied or theoretical researchers will also find this book beneficial.\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e  \u003cp\u003e\u003cstrong\u003eFaming Liang\u003c\/strong\u003e, Associate Professor, Department of Statistics, Texas A\u0026amp;M University. \u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eChuanhai Liu\u003c\/strong\u003e, Professor, Department of Statistics, Purdue University. \u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eRaymond J. Carroll\u003c\/strong\u003e, Distinguished Professor, Department of Statistics, Texas A\u0026amp;M University. \u003c\/p\u003e\n\u003c\/div\u003e\u003cbr\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublication Date: \u003c\/td\u003e\n\u003ctd\u003e30 August 2010\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublisher: \u003c\/td\u003e\n\u003ctd\u003eWiley\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eImprint: \u003c\/td\u003e\n\u003ctd\u003eWiley\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eISBN-13: \u003c\/td\u003e\n\u003ctd\u003e9780470748268\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFormat: \u003c\/td\u003e\n\u003ctd\u003eHardback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePage Count: \u003c\/td\u003e\n\u003ctd\u003e384\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWeight (oz): \u003c\/td\u003e\n\u003ctd\u003e26.0\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":44379873869964,"sku":"9780470748268","price":128.66,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9780470748268.jpg?v=1780114063","url":"https:\/\/lateknightbooks.com\/products\/9780470748268","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}