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Aggiungi al carrelloPaperback. Condizione: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
EUR 24,88
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Aggiungi al carrelloPaperback. Condizione: Fair. No Jacket. Former library book; Readable copy. Pages may have considerable notes/highlighting. ~ ThriftBooks: Read More, Spend Less 0.75.
EUR 48,36
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Aggiungi al carrelloPaperback. Condizione: new. Prompt service guaranteed.
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Aggiungi al carrelloPaperback. Condizione: New.
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Aggiungi al carrelloPaperback. Condizione: New. 2010th Edition. Special order direct from the distributor.
EUR 79,75
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Computational techniques based on simulation have now become an essential part of the statistician's toolbox. It is thus crucial to provide statisticians with a practical understanding of those methods, and there is no better way to develop intuition and skills for simulation than to use simulation to solve statistical problems. Introducing Monte Carlo Methods with R covers the main tools used in statistical simulation from a programmer's point of view, explaining the R implementation of each simulation technique and providing the output for better understanding and comparison. While this book constitutes a comprehensive treatment of simulation methods, the theoretical justification of those methods has been considerably reduced, compared with Robert and Casella (2004). Similarly, the more exploratory and less stable solutions are not covered here.This book does not require a preliminary exposure to the R programming language or to Monte Carlo methods, nor an advanced mathematical background. While many examples are set within a Bayesian framework, advanced expertise in Bayesian statistics is not required. The book covers basic random generation algorithms, Monte Carlo techniques for integration and optimization, convergence diagnoses, Markov chain Monte Carlo methods, including Metropolis {Hastings and Gibbs algorithms, and adaptive algorithms. All chapters include exercises and all R programs are available as an R package called mcsm. The book appeals to anyone with a practical interest in simulation methods but no previous exposure. It is meant to be useful for students and practitioners in areas such as statistics, signal processing, communications engineering, control theory, econometrics, finance and more. The programming parts are introduced progressively to be accessible to any reader.
Editore: Springer, Use R !, 2010
Da: Rometti Vincent, Nice, Francia
EUR 35,00
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Aggiungi al carrelloCouverture souple. Condizione: Très bon. Springer, Use R !, 2010. In-8 broché, XIX+283pp. Très bon état.
EUR 138,33
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Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
EUR 128,55
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Aggiungi al carrelloPaperback. Condizione: Like New. Like New. book.
EUR 160,57
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Editore: New York. Springer. 2010., 2010
Da: Antiquariat am Flughafen, Berlin, Germania
EUR 49,95
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Aggiungi al carrelloSoftcover. Condizione: sehr gut. Softcover. gr. 8°. xix, 283 pages. with illustrations and tables. less marks of use. good condition. Series Use R!". in englischer Sprache (in english).
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 74,89
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Computational techniques based on simulation have now become an essential part of the statistician's toolbox. It is thus crucial to provide statisticians with a practical understanding of those methods, and there is no better way to develop intuition and skills for simulation than to use simulation to solve statistical problems. Introducing Monte Carlo Methods with R covers the main tools used in statistical simulation from a programmer's point of view, explaining the R implementation of each simulation technique and providing the output for better understanding and comparison. While this book constitutes a comprehensive treatment of simulation methods, the theoretical justification of those methods has been considerably reduced, compared with Robert and Casella (2004). Similarly, the more exploratory and less stable solutions are not covered here.This book does not require a preliminary exposure to the R programming language or to Monte Carlo methods, nor an advanced mathematical background. While many examples are set within a Bayesian framework, advanced expertise in Bayesian statistics is not required. The book covers basic random generation algorithms, Monte Carlo techniques for integration and optimization, convergence diagnoses, Markov chain Monte Carlo methods, including Metropolis {Hastings and Gibbs algorithms, and adaptive algorithms. All chapters include exercises and all R programs are available as an R package called mcsm. The book appeals to anyone with a practical interest in simulation methods but no previous exposure. It is meant to be useful for students and practitioners in areas such as statistics, signal processing, communications engineering, control theory, econometrics, finance and more. The programming parts are introduced progressively to be accessible to any reader. 284 pp. Englisch.
Editore: Springer-Verlag New York Inc., 2009
ISBN 10: 1441915753 ISBN 13: 9781441915757
Lingua: Inglese
Da: THE SAINT BOOKSTORE, Southport, Regno Unito
EUR 92,62
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Aggiungi al carrelloPaperback / softback. Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 471.
Da: moluna, Greven, Germania
EUR 66,44
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Aggiungi al carrelloKartoniert / Broschiert. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The first book to present modern Monte Carlo and Markov Chain Monte Carlo (MCMC) methods from a practical perspective through a guided implementation in the R languageAll concepts are carefully described with the abstract theoretical background re.