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Introduction to Probability Simulation and Gibbs Sampling with R

Introduction to Probability Simulation and Gibbs Sampling with R

Introduction to Probability Simulation and Gibbs Sampling with R Paperback - 2010 - 2010th Edition

by Eric A. Suess

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Paperback. New. New Book; Fast Shipping from UK; Not signed; Not First Edition; 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
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Details

  • Title Introduction to Probability Simulation and Gibbs Sampling with R
  • Author Eric A. Suess
  • Binding Paperback
  • Edition number 2010th
  • Edition 2010
  • Condition New
  • Pages 307
  • Volumes 1
  • Language ENG
  • Publisher Springer, New York, NY
  • Publication date 2010-06-15
  • Illustrated Yes
  • Features Bibliography, Illustrated, Index, Maps
  • Bookseller's Inventory # ria9780387402734_inp
  • ISBN 9780387402734 / 038740273X
  • Weight 1 lbs (0.45 kg)
  • Dimensions 9.21 x 6.14 x 0.68 in (23.39 x 15.60 x 1.73 cm)
  • Themes
    • Aspects (Academic): Science/Technology Aspects
  • Category Mathematics
  • Library of Congress subjects Sampling (Statistics), R (Computer program language)
  • Library of Congress Catalogue Number 2010928331
  • Dewey Decimal Code 519.52
  • Quantity available 784

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Reader reviews for Introduction to Probability Simulation and Gibbs Sampling with R

From the publisher

Simulation has become a basic tool for the practice of applied probability and statistics. This is the first presentation of the Gibbs Sampler at an elementary level. The audience will be beginning graduate students in statistics and operations research.

From the rear cover

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.

About the author

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.
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