dc.contributor.author Robert, Christian P. dc.contributor.author Casella, George dc.date.accessioned 2010-01-11T10:26:12Z dc.date.available 2010-01-11T10:26:12Z dc.date.issued 2009 dc.identifier.isbn 978-1-4419-1575-7 en dc.identifier.uri https://basepub.dauphine.fr/handle/123456789/2862 dc.description Solutions des exercices proposés dans cet ouvrage librement accessibles à http://fr.arxiv.org/abs/1001.2906 dc.language.iso en en dc.subject Statistical Computing en dc.subject Simulation of Random Variables en dc.subject Probability and Statistics en dc.subject Number systems en dc.subject Monte Carlo Methods en dc.subject.ddc 519 en dc.title Introducing Monte Carlo Methods with R en dc.type Ouvrage dc.description.abstracten 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. en 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. dc.publisher.name Springer en dc.identifier.citationpages 284 en dc.relation.ispartofseriestitle Use R en dc.description.sponsorshipprivate oui en dc.subject.ddclabel Probabilités et mathématiques appliquées en dc.identifier.citationdate 2009
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