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＜電子ブック＞
Monte Carlo Statistical Methods / by Christian Robert, George Casella
(Springer Texts in Statistics. ISSN:21974136)

版	2nd ed. 2004.
出版者	New York, NY : Springer New York : Imprint: Springer
出版年	2004
本文言語	英語
大きさ	XXX, 649 p : online resource
冊子体	Monte Carlo statistical methods / Christian P. Robert, George Casella
著者標目	*Robert, Christian author Casella, George author SpringerLink (Online service)
件　名	LCSH:Statistics  LCSH:Mathematical statistics -- Data processing  全ての件名で検索 LCSH:Computer science -- Mathematics  全ての件名で検索 LCSH:Mathematical statistics FREE:Statistical Theory and Methods FREE:Statistics and Computing FREE:Probability and Statistics in Computer Science
一般注記	1 Introduction -- 2 Random Variable Generation -- 3 Monte Carlo Integration -- 4 Controling Monte Carlo Variance -- 5 Monte Carlo Optimization -- 6 Markov Chains -- 7 The Metropolis—Hastings Algorithm -- 8 The Slice Sampler -- 9 The Two-Stage Gibbs Sampler -- 10 The Multi-Stage Gibbs Sampler -- 11 Variable Dimension Models and Reversible Jump Algorithms -- 12 Diagnosing Convergence -- 13 Perfect Sampling -- 14 Iterated and Sequential Importance Sampling -- A Probability Distributions -- B Notation -- B.1 Mathematical -- B.2 Probability -- B.3 Distributions -- B.4 Markov Chains -- B.5 Statistics -- B.6 Algorithms -- References -- Index of Names -- Index of Subjects Monte Carlo statistical methods, particularly those based on Markov chains, are now an essential component of the standard set of techniques used by statisticians. This new edition has been revised towards a coherent and flowing coverage of these simulation techniques, with incorporation of the most recent developments in the field. In particular, the introductory coverage of random variable generation has been totally revised, with many concepts being unified through a fundamental theorem of simulation There are five completely new chapters that cover Monte Carlo control, reversible jump, slice sampling, sequential Monte Carlo, and perfect sampling. There is a more in-depth coverage of Gibbs sampling, which is now contained in three consecutive chapters. The development of Gibbs sampling starts with slice sampling and its connection with the fundamental theorem of simulation, and builds up to two-stage Gibbs sampling and its theoretical properties. A third chapter covers the multi-stage Gibbs sampler and its variety of applications. Lastly, chapters from the previous edition have been revised towards easier access, with the examples getting more detailed coverage. This textbook is intended for a second year graduate course, but will also be useful to someone who either wants to apply simulation techniques for the resolution of practical problems or wishes to grasp the fundamental principles behind those methods. The authors do not assume familiarity with Monte Carlo techniques (such as random variable generation), with computer programming, or with any Markov chain theory (the necessary concepts are developed in Chapter 6). A solutions manual, which covers approximately 40% of the problems, is available for instructors who require the book for a course. Christian P. Robert is Professor of Statistics in the Applied Mathematics Department at Université Paris Dauphine, France. He is also Head of the Statistics Laboratoryat the Center for Research in Economics and Statistics (CREST) of the National Institute for Statistics and Economic Studies (INSEE) in Paris, and Adjunct Professor at Ecole Polytechnique. He has written three other books and won the 2004 DeGroot Prize for The Bayesian Choice, Second Edition, Springer 2001. He also edited Discretization and MCMC Convergence Assessment, Springer 1998. He has served as associate editor for the Annals of Statistics, Statistical Science and the Journal of the American Statistical Association. He is a fellow of the Institute of Mathematical Statistics, and a winner of the Young Statistician Award of the Société de Statistique de Paris in 1995. George Casella is Distinguished Professor and Chair, Department of Statistics, University of Florida. He has served as the Theory and Methods Editor of the Journal of the American Statistical Association and Executive Editor of Statistical Science. He has authored three other textbooks: Statistical Inference, Second Edition, 2001, with Roger L. Berger; Theory of Point Estimation, 1998, with Erich Lehmann; and Variance Components, 1992, with Shayle R. Searle and Charles E. McCulloch. He is a fellow of the Institute of Mathematical Statistics and the American Statistical Association, and an elected fellow of the International Statistical Institute.  Accessibility summary: This PDF is not accessible. It is based on scanned pages and does not support features such as screen reader compatibility or described non-text content (images, graphs etc). However, it likely supports searchable and selectable text based on OCR (Optical Character Recognition). Users with accessibility needs may not be able to use this content effectively. Please contact us at accessibilitysupport@springernature.com if you require assistance or an alternative format Inaccessible, or known limited accessibility No reading system accessibility options actively disabled Publisher contact for further accessibility information: accessibilitysupport@springernature.com HTTP:URL=https://doi.org/10.1007/978-1-4757-4145-2

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