Logo - springer
Slogan - springer

Mathematics - Probability Theory and Stochastic Processes | Frontiers of Statistical Decision Making and Bayesian Analysis - In Honor of James O. Berger

Frontiers of Statistical Decision Making and Bayesian Analysis

In Honor of James O. Berger

Chen, M.-H., Müller, P., Sun, D., Ye, K., Dey, D.K. (Eds.)

2010, XXIII, 631 p.

Available Formats:
eBook
Information

Springer eBooks may be purchased by end-customers only and are sold without copy protection (DRM free). Instead, all eBooks include personalized watermarks. This means you can read the Springer eBooks across numerous devices such as Laptops, eReaders, and tablets.

You can pay for Springer eBooks with Visa, Mastercard, American Express or Paypal.

After the purchase you can directly download the eBook file or read it online in our Springer eBook Reader. Furthermore your eBook will be stored in your MySpringer account. So you can always re-download your eBooks.

 
$69.99

(net) price for USA

ISBN 978-1-4419-6944-6

digitally watermarked, no DRM

Included Format: PDF

download immediately after purchase


learn more about Springer eBooks

add to marked items

Hardcover
Information

Hardcover version

You can pay for Springer Books with Visa, Mastercard, American Express or Paypal.

Standard shipping is free of charge for individual customers.

 
$99.00

(net) price for USA

ISBN 978-1-4419-6943-9

free shipping for individuals worldwide

usually dispatched within 3 to 5 business days


add to marked items

Softcover
Information

Softcover (also known as softback) version.

You can pay for Springer Books with Visa, Mastercard, American Express or Paypal.

Standard shipping is free of charge for individual customers.

 
$99.00

(net) price for USA

ISBN 978-1-4899-9201-7

free shipping for individuals worldwide

usually dispatched within 3 to 5 business days


add to marked items

  • A concise update on the topics which are currently the most active areas of Bayesian research
  • Written by experts who are contributing to this research
  • Makes diverse research areas accessible to any reader who is familiar with the basics of the Bayesian approach
Research in Bayesian analysis and statistical decision theory is rapidly expanding and diversifying, making it increasingly more difficult for any single researcher to stay up to date on all current research frontiers. This book provides a review of current research challenges and opportunities. While the book can not exhaustively cover all current research areas, it does include some exemplary discussion of most research frontiers. Topics include objective Bayesian inference, shrinkage estimation and other decision based estimation, model selection and testing, nonparametric Bayes, the interface of Bayesian and frequentist inference, data mining and machine learning, methods for categorical and spatio-temporal data analysis and posterior simulation methods. Several major application areas are covered: computer models, Bayesian clinical trial design, epidemiology, phylogenetics, bioinformatics, climate modeling and applications in political science, finance and marketing. As a review of current research in Bayesian analysis the book presents a balance between theory and applications. The lack of a clear demarcation between theoretical and applied research is a reflection of the highly interdisciplinary and often applied nature of research in Bayesian statistics. The book is intended as an update for researchers in Bayesian statistics, including non-statisticians who make use of Bayesian inference to address substantive research questions in other fields. It would also be useful for graduate students and research scholars in statistics or biostatistics who wish to acquaint themselves with current research frontiers. Ming-Hui Chen is Professor of Statistics at the University of Connecticut; Dipak K. Dey is Head and Professor of Statistics at the University of Connecticut; Peter Müller is Professor of Biostatistics at the University of Texas M. D. Anderson Cancer Center; Dongchu Sun is Professor of Statistics at the University of Missouri- Columbia; and Keying Ye is Professor of Statistics at the University of Texas at San Antonio.

Content Level » Research

Keywords » Biostatistics - Computer simulation - Decision problems - Monte Carlo method - Objective Bayesian inference - STATISTICA - bayesian statistics - data analysis

Related subjects » Computational Science & Engineering - Probability Theory and Stochastic Processes - Statistical Theory and Methods

Table of contents 

Objective Bayesian Inference with Applications.- Bayesian Decision Based Estimation and Predictive Inference.- Bayesian Model Selection and Hypothesis Tests.- Bayesian Inference for Complex Computer Models.- Bayesian Nonparametrics and Semi-parametrics.- Bayesian Influence and Frequentist Interface.- Bayesian Clinical Trials.- Bayesian Methods for Genomics, Molecular and Systems Biology.- Bayesian Data Mining and Machine Learning.- Bayesian Inference in Political Science, Finance, and Marketing Research.- Bayesian Categorical Data Analysis.- Bayesian Geophysical, Spatial and Temporal Statistics.- Posterior Simulation and Monte Carlo Methods.

Popular Content within this publication 

 

Articles

Read this Book on Springerlink

Services for this book

New Book Alert

Get alerted on new Springer publications in the subject area of Probability Theory and Stochastic Processes.