000 04115nam a22004575i 4500
001 978-0-387-68765-0
003 DE-He213
005 20140220084454.0
007 cr nn 008mamaa
008 100528s2010 xxu| s |||| 0|eng d
020 _a9780387687650
_9978-0-387-68765-0
024 7 _a10.1007/978-0-387-68765-0
_2doi
050 4 _aQA276-280
072 7 _aPBT
_2bicssc
072 7 _aMAT029000
_2bisacsh
082 0 4 _a519.5
_223
100 1 _aSuess, Eric A.
_eauthor.
245 1 0 _aIntroduction to Probability Simulation and Gibbs Sampling with R
_h[electronic resource] /
_cby Eric A. Suess, Bruce E. Trumbo.
264 1 _aNew York, NY :
_bSpringer New York,
_c2010.
300 _aXIII, 307p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aUse R ;
_v0
505 0 _aIntroductory 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.
520 _aThe 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.
650 0 _aStatistics.
650 0 _aMathematical statistics.
650 1 4 _aStatistics.
650 2 4 _aStatistical Theory and Methods.
650 2 4 _aStatistics and Computing/Statistics Programs.
700 1 _aTrumbo, Bruce E.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9780387402734
830 0 _aUse R ;
_v0
856 4 0 _uhttp://dx.doi.org/10.1007/978-0-387-68765-0
912 _aZDB-2-SMA
999 _c109763
_d109763