Ebook: Tools for Statistical Inference: Observed Data and Data Augmentation Methods

From the reviews: The purpose of the book under review is to give a survey of methods for the Bayesian or likelihood-based analysis of data. The author distinguishes between two types of methods: the observed data methods and the data augmentation ones. The observed data methods are applied directly to the likelihood or posterior density of the observed data. The data augmentation methods make use of the special "missing" data structure of the problem. They rely on an augmentation of the data which simplifies the likelihood or posterior density. #Zentralblatt für Mathematik#

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From the reviews: The purpose of the book under review is to give a survey of methods for the Bayesian or likelihood-based analysis of data. The author distinguishes between two types of methods: the observed data methods and the data augmentation ones. The observed data methods are applied directly to the likelihood or posterior density of the observed data. The data augmentation methods make use of the special "missing" data structure of the problem. They rely on an augmentation of the data which simplifies the likelihood or posterior density. #Zentralblatt fur Mathematik#

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From the reviews: The purpose of the book under review is to give a survey of methods for the Bayesian or likelihood-based analysis of data. The author distinguishes between two types of methods: the observed data methods and the data augmentation ones. The observed data methods are applied directly to the likelihood or posterior density of the observed data. The data augmentation methods make use of the special "missing" data structure of the problem. They rely on an augmentation of the data which simplifies the likelihood or posterior density. #Zentralblatt fur Mathematik#
Content:
Front Matter....Pages I-VI
Introduction to Problems & Techniques....Pages 1-5
Observed Data Techniques-Normal Approximation....Pages 6-15
Observed Data Techniques - Approximations Based on Numerical Integration, Laplace Expansions, Monte Carlo and Importance Sampling....Pages 16-29
The EM Algorithm....Pages 30-46
The Data Augmentation Algorithm....Pages 47-88
The Gibbs Sampler....Pages 89-107
Back Matter....Pages 108-113

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From the reviews: The purpose of the book under review is to give a survey of methods for the Bayesian or likelihood-based analysis of data. The author distinguishes between two types of methods: the observed data methods and the data augmentation ones. The observed data methods are applied directly to the likelihood or posterior density of the observed data. The data augmentation methods make use of the special "missing" data structure of the problem. They rely on an augmentation of the data which simplifies the likelihood or posterior density. #Zentralblatt fur Mathematik#
Content:
Front Matter....Pages I-VI
Introduction to Problems & Techniques....Pages 1-5
Observed Data Techniques-Normal Approximation....Pages 6-15
Observed Data Techniques - Approximations Based on Numerical Integration, Laplace Expansions, Monte Carlo and Importance Sampling....Pages 16-29
The EM Algorithm....Pages 30-46
The Data Augmentation Algorithm....Pages 47-88
The Gibbs Sampler....Pages 89-107
Back Matter....Pages 108-113
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