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Sinopsis
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.
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Acerca del autor
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.
De la contraportada
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.
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Introduction to Probability Simulation and Gibbs Sampling with R
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Springer New York, 2010
ISBN 10: 038740273X
ISBN 13: 9780387402734
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Introduction to Probability Simulation and Gibbs Sampling with R (Use R!)
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Paperback. Condición: very good. xiii, 307p, diagrams; 24 cm. Probabilities -- Simulation methods. R (Computer program language) Sampling (Statistics) Probabilities -- Simulation methods. R (Computer program language) Sampling (Statistics) Gibbs-sampling R Simulation Stichprobennahme Wahrscheinlichkeit Wahrscheinlichkeitsverteilung. Sparse hilites in yellow to approx 3-4 pages only; else tight clean and very good(++). Nº de ref. del artículo: 92634
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Introduction to Probability Simulation and Gibbs Sampling with R (Use R!)
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Springer (edition 2010), 2010
ISBN 10: 038740273X
ISBN 13: 9780387402734
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Probability simulation using R inlcuding the simulations of the Law of Large numbers and the Central Limit TheoremIntroduces the most common methods of Monte Carlo integration using R. Gibbs sampling introduced using R and WinBUGS to obtain interval estima. Nº de ref. del artículo: 5910295
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Introduction to Probability Simulation and Gibbs Sampling with R
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Springer New York Jun 2010, 2010
ISBN 10: 038740273X
ISBN 13: 9780387402734
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 307 pp. Englisch. Nº de ref. del artículo: 9780387402734
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Introduction to Probability Simulation and Gibbs Sampling With R
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