New York, NY Springer 2010 Trade paperback New. Trade paperback (US). Glued binding. 494 p. Contains: Unspecified, Illustrations, black & white, Figures. Springer Statistics.

ISBN10: 144192194X, ISBN13: 9781441921949, [publisher: Springer] Softcover 492 pages. 9.00x6.10x1.20 inches. In Stock.
[Exeter, United Kingdom] [Publication Year: 2006]

ISBN10: 144192194X, ISBN13: 9781441921949, [publisher: Springer] Softcover
[Dallas, TX, U.S.A.] [Publication Year: 2010]

ISBN10: 144192194X, ISBN13: 9781441921949, [publisher: Springer] Softcover Book is in NEW condition.
[Hawthorne, CA, U.S.A.] [Publication Year: 2010]

ISBN10: 144192194X, ISBN13: 9781441921949, [publisher: Springer] Softcover
[Bayonne, NJ, U.S.A.] [Publication Year: 2010]
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ISBN10: 144192194X, ISBN13: 9781441921949, [publisher: Springer] Softcover Buy with confidence! Book is in good condition with minor wear to the pages, binding, and minor marks within
[Amherst, NY, U.S.A.] [Publication Year: 2010]

ISBN10: 144192194X, ISBN13: 9781441921949, [publisher: Springer] Softcover New! This book is in the same immaculate condition as when it was published
[Tucson, AZ, U.S.A.] [Publication Year: 2010]

ISBN10: 144192194X, ISBN13: 9781441921949, [publisher: Springer New York] Softcover Druck auf Anfrage Neuware - Printed after ordering - The prominence of finite mixture modelling is greater than ever. Many important statistical topics like clustering data, outlier treatment, or dealing with unobserved heterogeneity involve finite mixture models in some way or other. The area of potential applications goes beyond simple data analysis and extends to regression analysis and to non-linear time series analysis using Markov switching models.For more than the hundred years since Karl Pearson showed in 1894 how to estimate the five parameters of a mixture of two normal distributions using the method of moments, statistical inference for finite mixture models has been a challenge to everybody who deals with them. In the past ten years, very powerful computational tools emerged for dealing with these models which combine a Bayesian approach with recent Monte simulation techniques based on Markov chains. This book reviews these techniques and covers the most recent advances in the field, among them bridge sampling techniques and reversible jump Markov chain Monte Carlo methods.It is the first time that the Bayesian perspective of finite mixture modelling is systematically presented in book form. It is argued that the Bayesian approach provides much insight in this context and is easily implemented in practice. Although the main focus is on Bayesian inference, the author ...
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