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Statistical Methods in Software Engineering: Reliability and Risk (Springer Series in Statistics)
Nozer D. Singpurwalla, Simon P. Wilson
Springer, 1999
This book establishes a framework for dealing with uncertainties in software engineering, and for using quantitative measures for decision making in this context. It brings in perspective the large body of work having statistical content that is relevant to software engineering. The audience is computer scientists, software engineers, and reliability analysts, who have some exposure to ...
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Handbook of Statistics, Volume 25: Bayesian Thinking, Modeling and Computation (Handbook of Statistics) 1 review
North Holland, 2006
another fine handbook of statistics
Professor Krishnaiah had the idea of creating a series of handbooks on statstics with each volume covering a major topic or branch of statistical methodology. The handbooks are intended to have research articles that may contain very new developments in the field but also include articles that ...
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The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation (Springer Texts in ...4 reviews
Christian P. Robert
Springer Verlag, New York, 2007
second edition of excellent treatise on Bayesian methods
+ excellent text on Bayesian methods in statistical decision theory
+ A thorough description of bayesian statistics
+ Why you should be bayesian
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Subjective and Objective Bayesian Statistics: Principles, Models, and Applications (Wiley Series in ...1 review
S. James Press
Wiley-Interscience, 2002
revision of a wonderful text updated
Jim Press has produced a second edition to a book that had a different title but most of the same content. I have reviewed the earlier edition for amazon. So I will only point out the additions. In the past twenty years there has been a revolution in Bayesian statistics due to the Markov Chain ...
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Bayesian Computation with R (Use R) 4 reviews
Jim Albert
Springer, 2008
more practicality added to Bayesian inference
+ Excellent book for self-starters
+ Fantastic Resource
Jim Albert is a great teacher and an excellent writer. The R language is becoming one of the most used languages by statistical researchers. This is because it has many similarities to S and can be used freely, Jim makes R easy to learn for statisticians in this book. One of the big ...
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Bayesian Methods: An Analysis for Statisticians and Interdisciplinary Researchers (Cambridge Series in ... 6 reviews
Thomas Leonard, John S. J. Hsu
Cambridge University Press, 2001
good graduate level text on Bayesian approaches to statistics
+ Demonstrates Application of Bayesian Methods to Problems
+ good graduate level intro to Bayesian methods
The authors provide a graduate level (masters level) text for Bayesian methods. In the first chapter they introduce Fisherian statistical concepts and emphasize the likelihood methods. As Bayesian methods are introduced they often show how similar they are to the Fisherian methods when the prior ...
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Bayesian Analysis in Statistics and Econometrics: Essays in Honor of Arnold Zellner (Wiley Series in ...
Wiley-Interscience, 1995
This book is a definitive work that captures the current state of knowledge of Bayesian Analysis in Statistics and Econometrics and attempts to move it forward. It covers such topics as foundations, forecasting inferential matters, regression, computation and applications.
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Probability Theory: The Logic of Science17 reviews
E. T. Jaynes
Cambridge University Press, 2003
unbelievably charming and intelligent
+ Thought provoking
+ Flawed gems
+ On first reading
+ Great hard to find information
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Bayesian Forecasting and Dynamic Models (Springer Series in Statistics)2 reviews
Mike West, Jeff Harrison
Springer, 1999
time series using the Bayesian approach
+ A really good way to master Dinamic linear models
A Bayesian approach is a natural way to deal with time series data. You construct a model based on past data and prior information and use the model to predict future values in the series. When the new observations come in the model can be updated (model parameters reestimated) and forecasts can ...
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Bayesian Methods for Finite Population Sampling (Monographs on Statistics and Applied Probability)1 review
Malay Ghosh, Glen Meeden
Chapman & Hall/CRC, 1997
sampling table
the sampling vs population table for the 99% confident interva
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An Introduction to Bayesian Analysis: Theory and Methods (Springer Texts in Statistics)
Jayanta K. Ghosh, Mohan Delampady, ...
Springer, 2006
This is a graduate-level textbook on Bayesian analysis blending modern Bayesian theory, methods, and applications. Starting from basic statistics, undergraduate calculus and linear algebra, ideas of both subjective and objective Bayesian analysis are developed to a level where real-life data can be analyzed using the current techniques of statistical computing. Advances in both ...
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Bayesian Core: A Practical Approach to Computational Bayesian Statistics (Springer Texts in Statistics)
Jean-Michel Marin, Christian P. Robert
Springer, 2007
This Bayesian modeling book is intended for practitioners and applied statisticians looking for a self-contained entry to computational Bayesian statistics. Focusing on standard statistical models and backed up by discussed real datasets available from the book website, it provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical ...
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Applied Bayesian and Classical Inference: The Case of the Federalist Papers (Springer Series in Statistics)2 reviews
Frederick Mosteller
Springer, 1984
A Classical Book
+ A Classical Book
The "Applied Bayesian and Classical Inference: The Case of the Federalist Papers" is a classical book, useful to everyone working on the fields of text categorization, authorship attribution and stylometry. The authors apply the Bayes Theorem to the disputed Federalist Papers problem, but alongside ...
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Bayesian Statistics: Principles, Models, and Applications (Wiley Series in Probability and Statistics)1 review
S. James Press
John Wiley & Sons, 1989
the best of several Bayesian texts by Press
Jim Press is Professor of Statistics at the University of California at Riverside. He has authored a number of fine books on Bayesian statistical methods. This one, however, is my favorite because he very clearly lays out the principles of the Bayesian paradigm, develops the models that are ...
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Bayesian Approaches to Clinical Trials and Health-Care Evaluation (Statistics in Practice)2 reviews
David J. Spiegelhalter, Keith R. Abrams, ...
Wiley, 2004
a welcome new book on an important topic
+ Bayes for Health Technology Assessment
Clinical research in the US is heavily regulated by the FDA to insure that safe and effective products including drugs vaccines and medical devices come to market and that the clinical trials expose products that either are unsafe, ineffective or both. In recent years with the advent of Bayesian ...
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Contemporary Bayesian Econometrics and Statistics (Wiley Series in Probability and Statistics)
John Geweke
Wiley-Interscience, 2005
Tools to improve decision making in an imperfect world This publication provides readers with a thorough understanding of Bayesian analysis that is grounded in the theory of inference and optimal decision making. Contemporary Bayesian Econometrics and Statistics provides readers with state-of-the-art simulation methods and models that are used to solve complex real-world problems. Armed ...
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Bayesian Methods: A Social and Behavioral Sciences Approach, Second Edition (Statistics in the Social and ... 12 reviews
Jeff Gill
Chapman & Hall/CRC, 2007
extremely well written introduction for social scientists
+ Required book for class
+ Bayes from a social scientist's perspective
+ Excellent Introduction for Social Scientists
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Bayes and Empirical Bayes Methods for Data Analysis, Second Edition3 reviews
Bradley. P. Carlin, Thomas A. Louis
Chapman & Hall/CRC, 2000
An good overview of the corps of the matter
+ a must if you use Bayesian methods
This book features a deep and focused lesson on Bayes and Empirical Bayes Methods. It goes through the key topics as conjugate priors, MCMC methods (non iteratives and iteratives as the well known Gibbs samplining and metropolitis hastings algorithms), model selection methods (as bayes factor) and ...
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Kendall's Advanced Theory of Statistics: Volume 2B: Bayesian Inference (Arnold Publication)1 review
Anthony O'Hagan, Jonathan Forster
A Hodder Arnold Publication, 2004
O'Hagan's Jewel
I found this book to be my constant reference. Like Tonny's narative style and the part on NIG Priors! Contains valuable contributions by the author hard to find elsewhere. Highly recommended!
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Monte Carlo Methods in Bayesian Computation (Springer Series in Statistics)10 reviews
Ming-Hui Chen, Qi-Man Shao, ...
Springer, 2001
MCMC methods presente for efficient and realistic application of Bayesian methods
+ extensive book on MCMC
+ two great books
With advances in computing and the rediscovery of Markov Chain Monte Carlo methods and their application to Bayesian methods, there have been a number of books written on this subject in recent years. What then distinguishes this text from the others?
Section 1.1 of the text "Aims" provides the ...
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