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008			150903s2010 xxu| o |||| 0|eng d
020			_a9780387687650 _99780387687650
024	7		_a10.1007/9780387687650 _2doi
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050		4	_aQA276-280
100	1		_aSuess, Eric A. _eautor _9304939
245	1	0	_aIntroduction to Probability Simulation and Gibbs Sampling with R / _cby Eric A. Suess, Bruce E. Trumbo.
264		1	_aNew York, NY : _bSpringer New York, _c2010.
300			_axiii, 307 páginas _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aUse R ; _v0
500			_aSpringer eBooks
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.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aTrumbo, Bruce E. _eautor _9304940
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9780387402734
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-0-387-68765-0 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999			_c280179 _d280179