Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions - Rilegato
Libro 31 di 160: Springer Series in Statistics
Sinossi
A unified introduction to a variety of computational algorithms for likelihood and Bayesian inference. This third edition expands the discussion of many of the techniques presented, and includes additional examples as well as exercise sets at the end of each chapter.
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Contenuti
1. Introduction.- Exercises.- 2. Normal Approximations to Likelihoods and to Posteriors.- 2.1. Likelihood/Posterior Density.- 2.2. Specification of the Prior.- 2.3. Maximum Likelihood.- 2.4. Normal-Based Inference.- 2.5. The ?-Method (Propagation of Errors).- 2.6. Highest Posterior Density Regions.- Exercises.- 3. Nonnormal Approximations to Likelihoods and Posteriors.- 3.1. Numerical Integration.- 3.2. Posterior Moments and Marginalization Based on Laplace’s Method.- 3.3. Monte Carlo Methods.- Exercises.- 4. The EM Algorithm.- 4.1. Introduction.- 4.2. Theory.- 4.3. EM in the Exponential Family.- 4.4. Standard Errors in the Context of EM.- 4.5. Monte Carlo Implementation of the E-Step.- 4.6. Acceleration of EM (Louis’ Turbo EM).- 4.7. Facilitating the M-Step.- Exercises.- 5. The Data Augmentation Algorithm.- 5.1. Introduction and Motivation.- 5.2. Computing and Sampling from the Predictive Distribution.- 5.3. Calculating the Content and Boundary of the HPD Region.- 5.4. Remarks on the General Implementation of the Data Augmentation Algorithm.- 5.5. Overview of the Convergence Theory of Data Augmentation.- 5.6. Poor Man’s Data Augmentation Algorithms.- 5.7. Sampling/Importance Resampling (SIR).- 5.8. General Imputation Methods.- 5.9. Further Importance Sampling Ideas.- 5.10. Sampling in the Context of Multinomial Data.- Exercises.- 6. Markov Chain Monte Carlo: The Gibbs Sampler and the Metropolis Algorithm.- 6.1. Introduction to the Gibbs Sampler.- 6.2. Examples.- 6.3. Assessing Convergence of the Chain.- 6.4. The Griddy Gibbs Sampler.- 6.5. The Metropolis Algorithm.- 6.6. Conditional Inference via the Gibbs Sampler.- Exercises.- References.
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Book by Tanner Martin A
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Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions (Springer Series in Statistics)
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Springer (edition 3rd), 1996
ISBN 10: 0387946888
ISBN 13: 9780387946887
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Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions (Springer Series in Statistics)
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Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions (Springer Series in Statistics)
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hardcover. Condizione: Very Good. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority! Codice articolo S_455702377
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Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions - Third Edition
Da: Karen Wickliff - Books, Columbus, OH, U.S.A.
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Hard Cover. Condizione: Very Good. 207pp, yellow hardback, VG, small scrape along edge of the front cover, figures, equations, references, subject index, practice exercises at the end of each chapter, "introduction to a variety of computational algorithms for Bayesian and likelihood inference". Codice articolo w130815397
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Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions
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Springer-Verlag New York Inc., 1996
ISBN 10: 0387946888
ISBN 13: 9780387946887
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Hardcover. Condizione: Très bon. Ancien livre de bibliothèque avec équipements. Edition 1996. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Very good. Former library book. Edition 1996. Ammareal gives back up to 15% of this item's net price to charity organizations. Codice articolo H-394-209
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Tools for Statistical Inference Methods for the Exploration of Posterior Distributions and Likelihood Functions
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Hardcover. Condizione: Very Good. 3rd. Minor shelf wear; sunning to edges of boards. Else a bright, clean copy. This book provides a unified introduction to a variety of computational algorithms for Bayesian and likelihood inference. In this third edition, I have attempted to expand the treatment of many of the techniques discussed. I have added some new examples, as well as included recent results. Exercises have been added at the end of each chapter. Prerequisites for this book include an understanding of mathematical statistics at the level of Bickel and Doksum (1977), some understanding of the Bayesian approach as in Box and Tiao (1973), some exposure to statistical models as found in McCullagh and NeIder (1989), and for Section 6. 6 some experience with condi tional inference at the level of Cox and Snell (1989). I have chosen not to present proofs of convergence or rates of convergence for the Metropolis algorithm or the Gibbs sampler since these may require substantial background in Markov chain theory that is beyond the scope of this book. However, references to these proofs are given. There has been an explosion of papers in the area of Markov chain Monte Carlo in the past ten years. I have attempted to identify key references-though due to the volatility of the field some work may have been missed. Codice articolo 2039314
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Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions (Springer Series in Statistics)
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Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions
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Tools for statistical inference methods for the exploration of posterior distributions and likelihood functions Martin A. Tanner.
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New York, Springer, 1998
ISBN 10: 0387946888
ISBN 13: 9780387946887
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Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions (Springer Series in Statistics) - Tanner, Martin A.
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