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Bayesian statistical decision theory

Most popular at the top

  • Bayesian Estimation and Trackingby Anton J. Haug

    Wiley 2012; US$ 133.00

    A practical approach to estimating and tracking dynamic systems in real-worl applications Much of the literature on performing estimation for non-Gaussian systems is short on practical methodology, while Gaussian methods often lack a cohesive derivation. Bayesian Estimation and Tracking addresses the gap in the field on both accounts, providing... more...

  • Bayesian Inferenceby William A Link; Richard J Barker

    Elsevier Science 2009; US$ 73.95

    This text is written to provide a mathematically sound but accessible and engaging introduction to Bayesian inference specifically for environmental scientists, ecologists and wildlife biologists. It emphasizes the power and usefulness of Bayesian methods in an ecological context. The advent of fast personal computers and easily available software... more...

  • Bayesian Logical Data Analysis for the Physical Sciencesby Phil Gregory

    Cambridge University Press 2005; US$ 56.00

    A clear exposition of the underlying concepts, containing large numbers of worked examples and problem sets, first published in 2005. more...

  • Bayesian Methodsby Jeff Gill

    CRC Press 2014; US$ 75.95

    An Update of the Most Popular Graduate-Level Introductions to Bayesian Statistics for Social Scientists Now that Bayesian modeling has become standard, MCMC is well understood and trusted, and computing power continues to increase, Bayesian Methods: A Social and Behavioral Sciences Approach, Third Edition focuses more on implementation details... more...

  • Bayesian Methods for Measures of Agreementby Lyle D. Broemeling

    CRC Press 2009; US$ 109.95

    Using WinBUGS to implement Bayesian inferences of estimation and testing hypotheses, Bayesian Methods for Measures of Agreement presents useful methods for the design and analysis of agreement studies. It focuses on agreement among the various players in the diagnostic process. The author employs a Bayesian approach to provide statistical inferences... more...

  • Bayesian Missing Data Problemsby Ming T. Tan; Guo-Liang Tian; Kai Wang Ng

    CRC Press 2009; US$ 109.95

    Bayesian Missing Data Problems: EM, Data Augmentation and Noniterative Computation presents solutions to missing data problems through explicit or noniterative sampling calculation of Bayesian posteriors. The methods are based on the inverse Bayes formulae discovered by one of the author in 1995. Applying the Bayesian approach to important real-world... more...

  • Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysisby Uffe B. Kjærulff; Anders L. Madsen

    Springer New York 2007; US$ 109.00

    This book provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. The theory and methods presented are illustrated through more than 140 examples. more...

  • Bayesian Nonparametric Data Analysisby Peter Müller; Fernando Andrés Quintana; Alejandro Jara; Tim Hanson

    Springer International Publishing 2015; US$ 109.00

    This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book?s structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric... more...

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

    Springer New York 2008; US$ 199.00

    This book is a reference collection of modern Bayesian methods in reliability for use by reliability practitioners. There are more than 70 illustrative examples, most of which utilize real-world data. It can also be used as a textbook and contains more than 160 exercises. 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...