{"product_id":"introducing-monte-carlo-methods-with-r-paperback","title":"Introducing Monte Carlo Methods with R - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eChristian Robert\u003c\/b\u003e (Author), \u003cb\u003eGeorge Casella\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eComputational 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. \u003cem\u003eIntroducing Monte Carlo Methods with R\u003c\/em\u003e 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.\u003c\/p\u003e \u003cp\u003eThis 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.\u003c\/p\u003e\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eComputational 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. \u003cem\u003eIntroducing Monte Carlo Methods with R\u003c\/em\u003e 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.\u003c\/p\u003e \u003cp\u003eThis 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.\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChristian P. Robert\u003c\/strong\u003e is Professor of Statistics at Université Paris Dauphine, and Head of the Statistics Laboratory of CREST, both in Paris, France. He has authored more than 150 papers in applied probability, Bayesian statistics and simulation methods. He is a fellow of the Institute of Mathematical Statistics and the recipient of an IMS Medallion. He has authored eight other books, including \u003cem\u003eThe Bayesian Choice\u003c\/em\u003e which received the ISBA DeGroot Prize in 2004, Monte Carlo Statistical Methods with George Casella, and \u003cem\u003eBayesian Core\u003c\/em\u003e with Jean-Michel Marin. He has served as Joint Editor of the \u003cem\u003eJournal of the Royal Statistical Society Series B\u003c\/em\u003e, as well as an associate editor for most major statistical journals, and was the 2008 ISBA President.\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eGeorge Casella\u003c\/strong\u003e is Distinguished Professor in the Department of Statistics at the University of Florida. He is active in both theoretical and applied statistics, is a fellow of the Institute of Mathematical Statistics and the American Statistical Association, and a Foreign Member of the Spanish Royal Academy of Sciences. He has served as Theory and Methods Editor of the \u003cem\u003eJournal of the American Statistical Association\u003c\/em\u003e, as Executive Editor of \u003cem\u003eStatistical Science\u003c\/em\u003e, and as Joint Editor of the \u003cem\u003eJournal of the Royal Statistical Society Series B\u003c\/em\u003e\u003cem\u003e.\u003c\/em\u003e In addition to books with Christian Robert, he has written \u003cem\u003eVariance Components\u003c\/em\u003e, 1992, with S.R. Searle and C.E. McCulloch; \u003cem\u003eStatistical Inference\u003c\/em\u003e, Second Edition, 2001, with Roger Berger; and \u003cem\u003eTheory of Point Estimation\u003c\/em\u003e, Second Edition, 1998, with Erich Lehmann. His latest book is \u003cem\u003eStatistical Design\u003c\/em\u003e 2008.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 284\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.7 x 9.1 x 6.1 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e December 10, 2009\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":44276352876646,"sku":"9781441915757","price":158.61,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0599\/7255\/0758\/files\/XkMkcngI6G9781441915757.webp?v=1766428340","url":"https:\/\/infinitylightwa.com\/products\/introducing-monte-carlo-methods-with-r-paperback","provider":"Infinity Light","version":"1.0","type":"link"}