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Tools for statistical inference : methods for the exploration of posterior distributions and likelihood functions

Author: Martin Abba Tanner
Publisher: New York : Springer-Verlag, ©1993.
Series: Springer series in statistics.
Edition/Format:   Print book : English : 2nd edView all editions and formats
Summary:

This is a unified presentation of a variety of computational algorithms which are used in likelihood and Bayesian inference. The text has been revised in order to expand many of the techniques  Read more...

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Additional Physical Format: Online version:
Tanner, Martin Abba, 1957-
Tools for statistical inference.
New York : Springer-Verlag, ©1993
(OCoLC)622923724
Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Martin Abba Tanner
ISBN: 0387940316 9780387940311 3540940316 9783540940319
OCLC Number: 27726547
Description: ix, 156 pages : illustrations ; 24 cm.
Contents: Ch. 1. Introduction --
Ch. 2. Normal Approximations to Likelihoods and to Posteriors --
2.1. Likelihood/Posterior Density --
2.2. Maximum Likelihood --
2.3. Normal-Based Inference --
2.4. The [delta]-Method (Propagation of Errors) --
2.5. Highest Posterior Density Regions --
Ch. 3. Nonnormal Approximations to Likelihoods and to Posteriors --
3.1. Conjugate Priors and Numerical Integration --
3.2. Posterior Moments and Marginalization Based on Laplace's Method --
3.3. Monte Carlo Methods --
Ch. 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) --
Ch. 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 --
Ch. 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.
Series Title: Springer series in statistics.
Responsibility: Martin A. Tanner.
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