James O. Berger. Statistical Decision Theory and Bayesian Analysis

Incorporating new and updated information, this second edition of THE bestselling text in Bayesian data analysis continues to emphasize practice over theory, describing how to conceptualize, perform, and critique statistical analyses from a Bayesian perspective. Its world-class authors provide guidance on all aspects of Bayesian data analysis and include examples of real statistical analyses, based on their own research, that demonstrate how to s...

Wiley series in probability and statistics. Wiley & Sons, Ltd, 2000. - 611 pages. This volume, first published in hardback in 1994, presents an overview of the foundations and key theoretical concepts of Bayesian Statistics. The world of Bayesian Statistics has been changing shape and growing in size rapidly and unpredictably - most notably in relation to developments in computational methods and the subsequent opening up of new application h...

2nd Edition. Wiley series in probability and statistics. Wiley & Sons, Ltd, 2006. - 598 pages. Contents: Introduction: The Bayesian Method, its Benefits and Implementation Bayesian Model Choice, Comparison and Checking The Major Densities and their Application Normal Linear Regression, General Linear Models and Log-Linear Models Hierarchical Priors for Pooling Strength and Overdispersed Regression Modelling Discrete Mixture Priors Multinomia...

Academic Press, 1967. - 396 pages. The theory of games is a part of the rich mathematical legacy left by John von Neumann, one of the outstanding mathematicians of our era. Although others — notably Emil Borel — preceded him in formulating a theory of games, it was von Neumann who with the publication in 1927 of a proof of the minimax theorem for finite games laid the foundation for the theory of games as it is known today. Von Neumann's work cu...

Walter de Gruyter, 2004. - 532 Pages. Recent developments show that probability methods have become a very powerful tool in such different areas as statistical physics, dynamical systems, Riemannian geometry, group theory, harmonic analysis, graph theory and computer science. This volume is an outcome of the special semester 2001 - Random Walks held at the Schr?dinger Institute in Vienna, Austria. It contains original research articles with no...

Charles Griffin & Company Limited, 1946. - 521 pages. PREFACE TO VOLUME II This volume falls into five sections. The first, comprising chapters 17 to 20, deals with Estimation. The second, comprising chapters 21, 23, 24 and 26 to 28, covers the Theory of Statistical Tests, including the Analysis of Variance and Multivariate Analysis. The third, consisting of chapter 22, deals with Regression Analysis and completes the account of statistical...

Издательство Prentice Hall, 2003, -704 pp. Bayesian networks are graphical structures for representing the probabilistic relationships among a large number of variables and doing probabilistic inference with those variables. During the 1980’s, a good deal of related research was done on developing Bayesian networks (belief networks, causal networks, influence diagrams), algorithms for performing inference with them, and applications that used th...

Morgan Kaufmann Publishers, 2009. - 400 pages. The Bayesian network is one of the most important architectures for representing and reasoning with multivariate probability distributions. When used in conjunction with specialized informatics, possibilities of real-world applications are achieved. Probabilistic Methods for BioInformatics explains the application of probability and statistics, in particular Bayesian networks, to genetics. This book...

Название: An Introduction to Statistical Methods and Data Analysis. Издательство: Brooks/Cole. Автор: R. Lyman Ott, Micheal T. Longnecker. Год: 2010. Количество страниц: 1296. Язык: English. Ott and Longnecker's AN INTRODUCTION TO STATISTICAL METHODS AND DATA ANALYSIS, Sixth Edition, provides a broad overview of statistical methods for readers who have little or no prior experience in statistics. The authors teach readers to solve problems encoun...

John Wiley & Sons (Asia) Pte Ltd, 2010. - 312 pages. Bayesian inference is a statistical process that quanti?es the degree of belief of hypothesis, events or values of parameters. Many Bayesian methods have been developed in various areas of science and engineering, especially in statistical physics, medical sciences, electrical engineering, and information sciences, etc. This book presents various applications in civil engineering, includin...