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dc.contributor.authorRobert, Christian P.
dc.contributor.authorCasella, George
dc.date.accessioned2010-01-11T10:26:12Z
dc.date.available2010-01-11T10:26:12Z
dc.date.issued2009
dc.identifier.isbn978-1-4419-1575-7en
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/2862
dc.descriptionSolutions des exercices proposés dans cet ouvrage librement accessibles à http://fr.arxiv.org/abs/1001.2906
dc.language.isoenen
dc.subjectStatistical Computingen
dc.subjectSimulation of Random Variablesen
dc.subjectProbability and Statisticsen
dc.subjectNumber systemsen
dc.subjectMonte Carlo Methodsen
dc.subject.ddc519en
dc.titleIntroducing Monte Carlo Methods with Ren
dc.typeOuvrage
dc.description.abstractenComputational 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.en
dc.publisher.nameSpringeren
dc.identifier.citationpages284en
dc.relation.ispartofseriestitleUse Ren
dc.description.sponsorshipprivateouien
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.identifier.citationdate2009


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