Publicado por Springer (edition 2010), 2010
ISBN 10: 038740273X ISBN 13: 9780387402734
Idioma: Inglés
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Añadir al carritoPaperback. 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(++).
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Publicado por Springer-Verlag New York Inc., New York, NY, 2010
ISBN 10: 038740273X ISBN 13: 9780387402734
Idioma: Inglés
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Añadir al carritoPaperback. Condición: new. Paperback. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Añadir al carritoTaschenbuch. Condición: Neu. Introduction to Probability Simulation and Gibbs Sampling with R | Eric A Suess (u. a.) | Taschenbuch | xiii | Englisch | 2010 | Springer New York | EAN 9780387402734 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Añadir al carritoTaschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.
Publicado por Springer-Verlag New York Inc., New York, NY, 2010
ISBN 10: 038740273X ISBN 13: 9780387402734
Idioma: Inglés
Librería: AussieBookSeller, Truganina, VIC, Australia
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Añadir al carritoPaperback. Condición: new. Paperback. 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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Publicado por Springer-Verlag New York Inc., 2010
ISBN 10: 038740273X ISBN 13: 9780387402734
Idioma: Inglés
Librería: THE SAINT BOOKSTORE, Southport, Reino Unido
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Añadir al carritoPaperback / softback. Condición: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 486.
Publicado por Springer New York Jun 2010, 2010
ISBN 10: 038740273X ISBN 13: 9780387402734
Idioma: Inglés
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
EUR 96,29
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Añadir al carritoTaschenbuch. 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.
Librería: moluna, Greven, Alemania
EUR 84,15
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Añadir al carritoCondición: 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.