𝔖 Scriptorium
✦   LIBER   ✦

πŸ“

Tools for Statistical Inference: Observed Data and Data Augmentation Methods

✍ Scribed by Martin A. Tanner (auth.)


Publisher
Springer-Verlag New York
Year
1991
Tongue
English
Leaves
117
Series
Lecture Notes in Statistics 67
Edition
1
Category
Library

⬇  Acquire This Volume

No coin nor oath required. For personal study only.

✦ Synopsis


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 fΓΌr Mathematik#

✦ Table of Contents


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

✦ Subjects


Statistics for Life Sciences, Medicine, Health Sciences


πŸ“œ SIMILAR VOLUMES


Statistical Inference for Engineers and
✍ Pierre Moulin; Venugopal V. Veeravalli πŸ“‚ Library πŸ“… 2019 πŸ› Cambridge University Press 🌐 English

This book is a mathematically accessible and up-to-date introduction to the tools needed to address modern inference problems in engineering and data science, ideal for graduate students taking courses on statistical inference and detection and estimation, and an invaluable reference for researchers

Statistical Inference for Engineers and
✍ Pierre Moulin, Venugopal V. Veeravalli πŸ“‚ Library πŸ“… 2018 πŸ› Cambridge University Press 🌐 English

This book is a mathematically accessible and up-to-date introduction to the tools needed to address modern inference problems in engineering and data science, ideal for graduate students taking courses on statistical inference and detection and estimation, and an invaluable reference for researchers

Probability Theory and Statistical Infer
✍ Aris Spanos πŸ“‚ Library πŸ“… 1999 πŸ› Cambridge University Press 🌐 English

This major new textbook is intended for students taking introductory courses in probability theory and statistical inference. The primary objective of this book is to establish the framework for the empirical modeling of observational (nonexperimental) data. The text is extremely student friendly, w

Probability Theory And Statistical Infer
✍ Aris Spanos πŸ“‚ Library πŸ“… 2019 πŸ› Cambridge University Press 🌐 English

Doubt over the trustworthiness of published empirical results is not unwarranted and is often a result of statistical mis-specification: invalid probabilistic assumptions imposed on data. Now in its second edition, this bestselling textbook offers a comprehensive course in empirical research methods