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Bayesian statistical decision theory

Most popular at the top

  • Multivariate Bayesian Statisticsby Daniel B. Rowe

    CRC Press 2002; US$ 159.95

    Introduction Part l: FUNDAMENTALS STATISTICAL DISTRIBUTIONS Scalar Distributions Vector Distributions Matrix Distributions INTRODUCTORY BAYESIAN STATISTICS Discrete Scalar Variables Continuous Scalar Variables Continuous Vector Variables Continuous Matrix Variables PRIOR DISTRIBUTIONS Vague Priors Conjugate Priors Generaliz ed Priors... more...

  • Handbook of Statisticsby Dipak K. Dey; C.R. Rao

    Elsevier Science 2005; US$ 325.00

    This volume describes how to develop Bayesian thinking, modelling and computation both from philosophical, methodological and application point of view. It further describes parametric and nonparametric Bayesian methods for modelling and how to use modern computational methods to summarize inferences using simulation. The book covers wide range of... more...

  • Bayesian Statistical Modellingby Peter Congdon

    Wiley 2007; US$ 118.00

    Bayesian methods combine the evidence from the data at hand with previous quantitative knowledge to analyse practical problems in a wide range of areas. The calculations were previously complex, but it is now possible to routinely apply Bayesian methods due to advances in computing technology and the use of new sampling methods for estimating parameters.... more...

  • Bayes Linear Statistics, Theory & Methodsby Michael Goldstein; David Wooff

    Wiley 2007; US$ 179.00

    Michael Goldstein, Professor of Statistics, Department of Mathematical Sciences, University of Durham Michael Goldstein has worked on and researched the Bayes linear approach for around 30 years, his general interests being in the foundations, methodology and applications of Bayesian/subjectivist approaches to statistics. He has an outstanding... more...

  • Optimized Bayesian Dynamic Advisingby Miroslav Karny

    Springer 2006; US$ 209.00

    Written by one of the world's leading groups in the area of Bayesian identification, control, and decision making, this book provides the theoretical and algorithmic basis of optimized probabilistic advising. It is accompanied by a CD that contains a specialized Matlab-based Mixtools toolbox, and examples illustrating the important areas. more...

  • Introduction to Bayesian Statisticsby Karl-Rudolf Koch

    Springer 2007; US$ 149.00

    The Introduction to Bayesian Statistics (2nd Edition) presents Bayes? theorem, the estimation of unknown parameters, the determination of confidence regions and the derivation of tests of hypotheses for the unknown parameters, in a manner that is simple, intuitive and easy to comprehend. The methods are applied to linear models, in models for a robust... more...

  • Bayesian Reliabilityby Michael S. Hamada; Alyson G. Wilson; C. Shane Reese; Harry F. Martz

    Springer 2008; US$ 169.00

    Bayesian Reliability presents modern methods and techniques for analyzing reliability data from a Bayesian perspective. The adoption and application of Bayesian methods in virtually all branches of science and engineering have significantly increased over the past few decades. This increase is largely due to advances in simulation-based computational... more...

  • Bayesian Methods for Measures of Agreementby Lyle D. Broemeling

    CRC Press 2009; US$ 109.95

    "This book is a welcome addition to the literature on Bayesian inference as it presents methods for the design and analysis of agreement studies. ? The approach presented by the author is novel and the novice will find a helpful introduction to Bayesian inference in an appendix. ? The text is readable and will form a valuable reference source. For... more...

  • Data Analysisby Devinderjit Sivia; John Skilling

    Oxford University Press, UK 2006; US$ 50.00

    One of the strengths of this book is the author's ability to motivate the use of Bayesian methods through simple yet effective examples. - Katie St. Clair MAA Reviews more...

  • Bayesian Computation with Rby

    Springer New York 2009; US$ 59.95

    There has been a dramatic growth in the development and application of Bayesian inferential methods. Some of this growth is due to the availability of powerful simulation-based algorithms to summarize posterior distributions. There has been also a growing interest in the use of the system R for statistical analyses. R's open source nature, free... more...