Bayesian Theory and Methods with Applications
โ Scribed by Vladimir Savchuk, Chris P. Tsokos (auth.)
- Publisher
- Atlantis Press
- Year
- 2011
- Tongue
- English
- Leaves
- 332
- Series
- Atlantis Studies in Probability and Statistics 1
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Synopsis
Bayesian methods are growing more and more popular, finding new practical applications in the fields of health sciences, engineering, environmental sciences, business and economics and social sciences, among others. This book explores the use of Bayesian analysis in the statistical estimation of the unknown phenomenon of interest. The contents demonstrate that where such methods are applicable, they offer the best possible estimate of the unknown. Beyond presenting Bayesian theory and methods of analysis, the text is illustrated with a variety of applications to real world problems.
โฆ Table of Contents
Front Matter....Pages i-xiv
General Questions of Bayes Theory....Pages 1-15
The Accepted Bayes Method of Estimation....Pages 17-46
The Methods of Parametric Bayes Estimation Based on Censored Samples....Pages 47-77
Nonparametric Bayes Estimation....Pages 79-121
Quasi-Parametric Bayes Estimates of the TTF Probability....Pages 123-162
Estimates of the TTF Probability under the Conditions of a Partial Prior Uncertainty....Pages 163-191
Empirical Bayes Estimates of Reliability....Pages 193-218
Theoretical Model for Engineering Reliability....Pages 219-246
Statistical Reliability Analysis Prior Bayes Estimates....Pages 247-276
Statistical Reliability Analysis Posterior Bayes Estimates....Pages 277-306
Back Matter....Pages 307-317
โฆ Subjects
Statistical Theory and Methods; Applications of Mathematics; Mathematical Modeling and Industrial Mathematics; Probability and Statistics in Computer Science; Biostatistics; Popular Science in Mathematics/Computer Science/Natural Science
๐ SIMILAR VOLUMES
The development of hierarchical models and Markov chain Monte Carlo (MCMC) techniques forms one of the most profound advances in Bayesian analysis since the 1970s and provides the basis for advances in virtually all areas of applied and theoretical Bayesian statistics. <br><br>This volume guides the
<P>Offering a rich diversity of models, Bayesian phylogenetics allows evolutionary biologists, systematists, ecologists, and epidemiologists to obtain answers to very detailed phylogenetic questions. Suitable for graduate-level researchers in statistics and biology, <STRONG>Bayesian Phylogenetics: M
<p><STRONG><P>For his excellent monograph, David Ardia won the Chorafas prize 2008 at the University of Fribourg Switzerland.</P></STRONG><P>This book presents methodologies for the Bayesian estimation of GARCH models and their application to financial risk management. The study of these models from