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      • [PDF] Understanding Computational Bayesian Statistics Free Download by William M. Bolstad | Publisher : Wiley | Category : Computers & Internet | Tags : Models, Computer, Behavior, Algorithm, Graphs, Functions | ISBN-10 : 0470046090 | ISBN-13 : 9780470046098
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      • Understanding Computational Bayesian Statistics by William M. Bolstad, Publisher : Wiley
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    • Complete Book Description
      • A hands-on introduction to computational statisticsfrom a Bayesian point of view

        Providing a solid grounding in statistics while uniquely covering the topics from a Bayesian perspective, Understanding Computational Bayesian Statisticssuccessfully guides readers through this new, cutting-edge approach. With its hands-on treatment of the topic, the book shows how samples can be drawn from the posterior distribution when the formula giving its shape is all that is known, and how Bayesian inferences can be based on these samples from the posterior. These ideas are illustrated on common statistical models, including the multiple linear regression model, the hierarchical mean model, the logistic regression model, and the proportional hazards model.

        The book begins with an outline of the similarities and differences between Bayesian and the likelihood approaches to statistics. Subsequent chapters present key techniques for using computer software to draw Monte Carlo samples from the incompletely known posterior distribution and performing the Bayesian inference calculated from these samples. Topics of coverage include:

        • Direct ways to draw a random sample from the posterior by reshaping a random sample drawn from an easily sampled starting distribution
        • The distributions from the one-dimensional exponential family
        • Markov chains and their long-run behavior
        • The Metropolis-Hastings algorithm
        • Gibbs sampling algorithm and methods for speeding up convergence
        • Markov chain Monte Carlo sampling

        Using numerous graphs and diagrams, the author emphasizes a step-by-step approach to computational Bayesian statistics. At each step, important aspects of application are detailed, such as how to choose a prior for logistic regression model, the Poisson regression model, and the proportional hazards model. A related Web site houses R functions and Minitab macros for Bayesian analysis and Monte Carlo simulations, and detailed appendices in the book guide readers through the use of these software packages.

        Understanding Computational Bayesian Statistics is an excellent book for courses on computational statistics at the upper-level undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners who use computer programs to conduct statistical analyses of data and solve problems in their everyday work.

        Table of Contents

        Chapter 1 Introduction to Bayesian Statistics
        Chapter 2 Monte Carlo Sampling from the Posterior
        Chapter 3 Bayesian Inference
        Chapter 4 Bayesian Statistics Using Conjugate Priors
        Chapter 5 Markov Chains
        Chapter 6 Markov Chain Monte Carlo Sampling from Posterior
        Chapter 7 Statistical Inference from a Markov Chain Monte Carlo Sample
        Chapter 8 Logistic Regression
        Chapter 9 Poisson Regression and Proportional Hazards Model
        Chapter 10 Gibbs Sampling and Hierarchical Models
        Chapter 11 Going Forward with Markov Chain Monte Carlo

        A Using the Included Minitab Macros
        B Using the Included R Functions

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    • Book Details
      • Book Name : Understanding Computational Bayesian Statistics

        Edition : 1

        Author : William M. Bolstad

        Publisher : Wiley

        Category : Computers & Internet

        ISBN-10 : 0470046090

        ISBN-13 : 9780470046098

        ASIN : 0470046090

        Pages : 336

        Language : English

        Publish Date : December 14, 2009
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