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Introducing Monte Carlo Methods with R de Christian Robert

Introducing Monte Carlo Methods with R

Autor Christian Robert, George Casella
en Limba Engleză Paperback – 10 dec 2009

Subliniem natura profund interdisciplinară a lucrării Introducing Monte Carlo Methods with R, un manual care transcende granițele statisticii pure pentru a oferi soluții computaționale vitale în domenii precum econometria, finanțele, procesarea semnalului și teoria controlului. Apreciem modul în care autorii Christian Robert și George Casella au recalibrat materialul pentru a servi nevoilor cercetătorilor și practicienilor care au nevoie de rezultate imediate, fără a impune un background matematic avansat sau experiență prealabilă în programarea R. Notăm cu interes progresia logică a conținutului, structurată în opt capitole esențiale. Volumul debutează cu elemente de bază în R și generarea variabilelor aleatorii, avansând sistematic către integrarea și optimizarea Monte Carlo, pentru a culmina cu metode complexe de tip Markov chain Monte Carlo (MCMC), inclusiv algoritmi Metropolis-Hastings și Gibbs. Această ediție din 2010 se distinge prin faptul că reduce considerabil partea teoretică în favoarea implementării, spre deosebire de lucrarea anterioară a autorilor, Monte Carlo Statistical Methods, care rămâne o referință mai densă și axată pe demonstrații formale. Comparativ cu An Introduction to Statistical Computing de Jochen Voss, care oferă o perspectivă teoretică asupra metodelor de eșantionare, volumul de față adoptă o abordare mult mai practică, centrată pe cod și pe pachetul R dedicat, `mcsm`. Dacă în The Bayesian Choice, Christian Robert se concentra pe fundamentele teoriei deciziei, aici asistăm la o aplicare tehnică, unde cadrul Bayesian este utilizat ca un vehicul pentru exemple practice, fără a fi un prerechizit. Este un instrument de lucru robust, unde output-ul programelor este prezentat direct pentru a facilita validarea rapidă a algoritmilor de către cititor.

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Specificații

ISBN-13: 9781441915757
ISBN-10: 1441915753
Pagini: 284
Ilustrații: XX, 284 p.
Dimensiuni: 154 x 233 x 20 mm
Greutate: 0.48 kg
Ediția:2010 edition
Editura: Springer
Locul publicării:New York, NY, United States

Public țintă

Research

De ce să citești această carte

Recomandăm această carte oricărui specialist care dorește să stăpânească simulările statistice prin practică directă. Câștigul principal este transformarea teoriei în cod R funcțional, facilitată de pachetul mcsm inclus. Este ideală pentru cercetătorii din inginerie sau finanțe care au nevoie de metode Monte Carlo pentru optimizare și integrare, dar preferă un stil de învățare bazat pe exemple de programare în detrimentul demonstrațiilor matematice exhaustive.

Despre autor

Christian Robert este un statistician de renume mondial, profesor la Universitatea Paris-Dauphine și membru senior al Institut Universitaire de France, cunoscut pentru contribuțiile sale majore în statistica Bayesiană. Alături de regretatul George Casella, care a fost profesor distins la University of Florida și un expert în inferență statistică, a format una dintre cele mai influente echipe editoriale în domeniul metodelor computaționale. Opera lor comună a definit standardele moderne pentru utilizarea simulărilor în statistică, combinând rigoarea academică cu utilitatea practică în software-ul statistic.

Cuprins

Basic R Programming.- Random Variable Generation.- Monte Carlo Integration.- Controlling and Accelerating Convergence.- Monte Carlo Optimization.- Metropolis#x2013;Hastings Algorithms.- Gibbs Samplers.- Convergence Monitoring and Adaptation for MCMC Algorithms.

Recenzii

From the reviews:
“Robert and Casella’s new book uses the programming language R, a favorite amongst (Bayesian) statisticians to introduce in eight chapters both basic and advanced Monte Carlo techniques … . The book could be used as the basic textbook for a semester long course on computational statistics with emphasis on Monte Carlo tools … . useful for (and should be next to the computer of) a large body of hands on graduate students, researchers, instructors and practitioners … .” (Hedibert Freitas Lopes, Journal of the American Statistical Association, Vol. 106 (493), March, 2011)
“Chapters focuses on MCMC methods the Metropolis–Hastings algorithm, Gibbs sampling, and monitoring and adaptation for MCMC algorithms. … There are exercises within and at the end of all chapters … . Overall, the level of the book makes it suitable for graduate students and researchers. Others who wish to implement Monte Carlo methods, particularly MCMC methods for Bayesian analysis will also find it useful.” (David Scott, International Statistical Review, Vol. 78 (3), 2010)
“The primary audience is graduate students in statistics, biostatistics, engineering, etc. who need to know how to utilize Monte Carlo simulation methods to analyze their experiments and/or datasets. … this text does an effective job of including a selection of Monte Carlo methods and their application to a broad array of simulation problems. … Anyone who is an avid R user and has need to integrate and/or optimize complex functions will find this text to be a necessary addition to his or her personal library.” (Dean V. Neubauer, Technometrics, Vol. 53 (2), May, 2011)

Textul de pe ultima copertă

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.
Christian P. Robert is Professor of Statistics at Université Paris Dauphine, and Head of the Statistics Laboratory of CREST, bothin 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 The Bayesian Choice which received the ISBA DeGroot Prize in 2004, Monte Carlo Statistical Methods with George Casella, and Bayesian Core with Jean-Michel Marin. He has served as Joint Editor of the Journal of the Royal Statistical Society Series B, as well as an associate editor for most major statistical journals, and was the 2008 ISBA President.
George Casella 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 Journal of the American Statistical Association, as Executive Editor of Statistical Science, and as Joint Editor of the Journal of the Royal Statistical Society Series B. In addition to books with Christian Robert, he has written Variance Components, 1992, with S.R. Searle and C.E. McCulloch; Statistical Inference, Second Edition, 2001, with Roger Berger; and Theory of Point Estimation, Second Edition, 1998, with Erich Lehmann. His latest book is Statistical Design 2008.

Caracteristici

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 language All concepts are carefully described with the abstract theoretical background replaced with a corresponding R program that the reader can use and modify at will The whole entire series of examples from the book is accompanied by a free R package called mcsm that allows for immediate experimentation Includes supplementary material: sn.pub/extras Request lecturer material: sn.pub/lecturer-material