Introduction to Probability Simulation and Gibbs Sampling with R

Häftad, Engelska, 2010

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Beskrivning

Simulation has become a basic tool for the practice of applied probability and statistics. The Gibbs Sampler is an especially important, but not widely understood simulation method. With a basic introduction to Monte Carlo simulation that provides enough background in other topics, students can understand the rationale for and use of the Gibbs Sampler as a practical simulation tool.

Produktinformation

Utgivningsdatum:2010-06-15
Mått:155 x 235 x 23 mm
Vikt:468 g
Format:Häftad
Språk:Engelska
Serie:Use R!
Antal sidor:307
Upplaga:2010
Förlag:Springer-Verlag New York Inc.
ISBN:9780387402734

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Mer om författaren

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.

Recensioner i media

From the reviews: "Suess and Trumbo's book 'Introduction to Probability Simulation and Gibbs Sampling with R,' part of the 'Use R!' series, fits precisely into this framework of learning by doing-and doing again, with different distributions, or different parameters, or under different scenarios. ... The book also contains an Appendix with an introduction to R, which should make it particularly attractive to students, who won't have to go to another source to learn about the basics. ... an overall very useful book." (Nicole Lazar, Technometrics, Vol. 53 (3), August, 2011)

Innehållsförteckning

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.- to Bayesian Estimation.- Using Gibbs Samplers to Compute Bayesian Posterior Distributions.- Using WinBUGS for Bayesian Estimation.- Appendix: Getting Started with R.