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Field name	Details
Dewey Class	519.5
Title	Introduction to Probability Simulation and Gibbs Sampling with R (EB) / by Eric A. Suess, Bruce E. Trumbo.
Author	Suess, Eric A.
Added Personal Name	Trumbo, Bruce E.
Other name(s)	SpringerLink (Online service)
Publication	New York, NY : Springer , 2010.
Physical Details	XIII, 307 pages : online resource.
Series	Use R ; 0
ISBN	DOI : 9780387687650
Summary Note	The first seven chapters use R for probability simulation and computation, including random number generation, numerical and Monte Carlo integration, and finding limiting distributions of Markov Chains with both discrete and continuous states. Applications include coverage probabilities of binomial confidence intervals, estimation of disease prevalence from screening tests, parallel redundancy for improved reliability of systems, and various kinds of genetic modeling. These initial chapters can be used for a non-Bayesian course in the simulation of applied probability models and Markov Chains. Chapters 8 through 10 give a brief introduction to Bayesian estimation and illustrate the use of Gibbs samplers to find posterior distributions and interval estimates, including some examples in which traditional methods do not give satisfactory results. WinBUGS software is introduced with a detailed explanation of its interface and examples of its use for Gibbs sampling for Bayesian estimation. No previous experience using R is required. An appendix introduces R, and complete R code is included for almost all computational examples and problems (along with comments and explanations). Noteworthy features of the book are its intuitive approach, presenting ideas with examples from biostatistics, reliability, and other fields; its large number of figures; and its extraordinarily large number of problems (about a third of the pages), ranging from simple drill to presentation of additional topics. Hints and answers are provided for many of the problems. These features make the book ideal for students of statistics at the senior undergraduate and at the beginning graduate levels. Eric A. Suess is Chair and Professor of Statistics and Biostatistics and Bruce E. Trumbo is Professor Emeritus of Statistics and Mathematics, both at California State University, East Bay. Professor Suess is experienced in applications of Bayesian methods and Gibbs sampling to epidemiology. Professor Trumbo is a fellow of the American Statistical Association and the Institute of Mathematical Statistics, and he is a recipient of the ASA Founders Award and the IMS Carver Medallion.:
Contents note	Introductory Examples: Simulation, Estimation, and Graphics -- Generating Random Numbers -- Monte Carlo Integration and Limit Theorems -- Sampling from Applied Probability Models -- Screening Tests -- Markov Chains with Two States -- Examples of Markov Chains with Larger State Spaces -- Introduction to Bayesian Estimation -- Using Gibbs Samplers to Compute Bayesian Posterior Distributions -- Using WinBUGS for Bayesian Estimation -- Appendix: Getting Started with R.
System details note	Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users).
Internet Site	http://dx.doi.org/10.1007/978-0-387-68765-0
Links to Related Works	Subject References: Mathematical Statistics . Statistical Theory and Methods . Statistics . Statistics and Computing/Statistics Programs . Authors: author . Suess, Eric A. . Trumbo, Bruce E. . Corporate Authors: SpringerLink (Online service) . Series: Use R . Classification: 519.5 .

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Introduction to Probability Simulation and Gibbs Sampling with R

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