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Introducing Monte Carlo methods with R / Christian P. Robert, George Casella.

By: Robert, Christian P 1961-.
Contributor(s): Casella, George.
Material type: materialTypeLabelBookSeries: Use R!: Publisher: New York : Springer, c2010Description: xix, 283 p. : ill. ; 24 cm.ISBN: 9781441915757 (pbk.); 1441915753 (pbk.); 1441915761 (ebk.); 9781441915764 (ebk.).Subject(s): Monte Carlo method -- Computer programs | Mathematical statistics -- Data processing | R (Computer program language) | Markov processes | Monte Carlo Method | Mathematical Computing | Monte Carlo-methode | R (computerprogramma) | Monte-Carlo-Simulation | R (Programm)DDC classification: 518.282 Online resources: Publisher Description and Content Page https://link.springer.com/book/10.1007/978-1-4419-1576-4
Contents:
Basic R Programming-Pages 1-39. Random Variable Generation -Pages 41-60. Monte Carlo Integration-Pages 61-88. Controlling and Accelerating Convergence-Pages 89-124. Monte Carlo Optimization-Pages 125-165. Metropolis–Hastings Algorithms-Pages 167-197. Gibbs Samplers-Pages 199-236. Convergence Monitoring and Adaptation for MCMC Algorithms-Pages 237-268.
Summary: Summary: 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. Taken from the Publisher Site.
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Item type Current location Call number Copy number Status Date due
Monograph Monograph Indian Institute of Management Udaipur
B1/2 		518.282 RCP (Browse shelf) 1 Available

Includes bibliographical references (p. [269]-274) and index.

Basic R Programming-Pages 1-39.
Random Variable Generation -Pages 41-60.
Monte Carlo Integration-Pages 61-88.
Controlling and Accelerating Convergence-Pages 89-124.
Monte Carlo Optimization-Pages 125-165.
Metropolis–Hastings Algorithms-Pages 167-197.
Gibbs Samplers-Pages 199-236.
Convergence Monitoring and Adaptation for MCMC Algorithms-Pages 237-268.

Summary:
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. Taken from the Publisher Site.

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