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Research and Markets: Bayesian Modeling Using WinBUGS - Report Available Now.

DUBLIN, Ireland -- Research and Markets ( has announced the addition of John Wiley John Wiley may refer to:
  • John Wiley & Sons, publishing company
  • John C. Wiley, American ambassador
  • John D. Wiley, Chancellor of the University of Wisconsin-Madison
  • John M. Wiley (1846–1912), U.S.
 and Sons Ltd's new report "Bayesian Modeling Using WinBUGS" to their offering.

The BUGS (Bayesian inference Bayesian inference is statistical inference in which evidence or observations are used to update or to newly infer the probability that a hypothesis may be true. The name "Bayesian" comes from the frequent use of Bayes' theorem in the inference process.  Using Gibbs Sampling) project is concerned with free, flexible software for the Bayesian analysis Bayesian analysis A decision-making analysis that '…permits the calculation of the probability that one treatment is superior based on the observed data and prior beliefs…subjectivity of beliefs is not a liability, but rather explicitly allows  of complex statistical models using Markov Chain Monte Carlo Markov chain Monte Carlo (MCMC) methods (which include random walk Monte Carlo methods), are a class of algorithms for sampling from probability distributions based on constructing a Markov chain that has the desired distribution as its equilibrium distribution.  (MCMC MCMC Markov Chain Monte Carlo
MCMC Malaysian Communications and Multimedia Commission
MCMC Mid-Continent Mapping Center
McMC McMaster-Carr
MCMC Marine Corps Maintenance Contractor
) methods. It details the various and commonly-used modeling techniques that are employed by statisticians Statisticians or people who made notable contributions to the theories of statistics, or related aspects of probability, or machine learning: A to E
  • Odd Olai Aalen (1947–)
  • Gottfried Achenwall (1719–1772)
  • Abraham Manie Adelstein (1916–1992)
 in a multitude of sciences such as; biostatistics and social science; actuarial science environments. This book presents the reader with a clear and easily accessible introduction to the use of WinBUGS programming techniques in a variety of Bayesian modeling settings. Emphasis is given to Generalized Linear Models (GLMs) familiar to most readers and researchers. Detailed explanations cover model building, prior specification, writing WinBUGS code, and analyzing and interpreting WinBUGS output. Also features comprehensive problems and examples.

Key Topics Covered:

* Preface.

* Acknowledgments.

* Acronyms.

* Introduction to Bayesian inference.

* Markov Chain Monte Carlo Algorithms in Bayesian Inference.

* WinBUGS Software: Introduction, Setup and Basic Analysis.

* WinBUGS Software: Illustration, Results, and Further Analysis.

* Introduction to Bayesian Models: Normal models.

* Incorporating Categorical Variables in Normal Models and Further Modeling Issues.

* Introduction to Generalized Linear Models: Binomial binomial (bī'nō`mēəl), polynomial expression (see polynomial) containing two terms, for example, x+y. The binomial theorem, or binomial formula, gives the expansion of the nth power of a binomial (x+  and Poisson Data.

* Models for Positive Continuous Data, Count Data, and Other GLM-Based Extensions.

* Bayesian Hierarchical Models.

* The Predictive Distribution and Model Checking.

* Bayesian Model and Variable Evaluation.

* Appendix A: Model Specification via Directed Acyclic Graphs: The Doodle Menu.

* Appendix B: The Batch Mode: Running a Model in the Background Using Scripts.

* Appendix C: Checking Convergence Using CODA/BOA.

* Appendix D: Notation Summary.

* References.

* Index.

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Publication:Business Wire
Article Type:Report
Date:Apr 6, 2009
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