In weniger als einer Minute können Sie mit dem Lesen von Introducing Monte Carlo Methods with R (Use R!) auf Ihrem Kindle beginnen. Sie haben noch keinen Kindle? Hier kaufen Oder fangen Sie mit einer unserer gratis Kindle Lese-Apps sofort an zu lesen.

An Ihren Kindle oder ein anderes Gerät senden

 
 
 

Kostenlos testen

Jetzt kostenlos reinlesen

An Ihren Kindle oder ein anderes Gerät senden

Jeder kann Kindle Bücher lesen  selbst ohne ein Kindle-Gerät  mit der KOSTENFREIEN Kindle App für Smartphones, Tablets und Computer.
Introducing Monte Carlo Methods with R (Use R!)
 
 

Introducing Monte Carlo Methods with R (Use R!) [Kindle Edition]

Christian P. Robert , George Casella

Kindle-Preis: EUR 38,78 Inkl. MwSt. und kostenloser drahtloser Lieferung über Amazon Whispernet

Weitere Ausgaben

Amazon-Preis Neu ab Gebraucht ab
Kindle Edition EUR 38,78  
Taschenbuch EUR 58,50  


Produktbeschreibungen

Pressestimmen

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)

Kurzbeschreibung

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.

Produktinformation


Mehr über den Autor

Entdecken Sie Bücher, lesen Sie über Autoren und mehr

Kundenrezensionen

Es gibt noch keine Kundenrezensionen auf Amazon.de
5 Sterne
4 Sterne
3 Sterne
2 Sterne
1 Sterne
Die hilfreichsten Kundenrezensionen auf Amazon.com (beta)
Amazon.com: 3.0 von 5 Sternen  3 Rezensionen
20 von 20 Kunden fanden die folgende Rezension hilfreich
4.0 von 5 Sternen Quite Good 14. Dezember 2011
Von Guy - Veröffentlicht auf Amazon.com
Format:Taschenbuch
I guess this text is supposed to be the applied, less theoretical, little brother of Monte Carlo Statistical Methods which was written by the same authors. The amount of material seems appropriate for a 1 semester crash-course in applications, and in my opinion it does this quite well. Proofs here are replaced either by heuristics or by nothing at all, which is fine for someone who just wants to run their algorithms without looking too far under the hood.

There is a corresponding R package which contains all the code from the examples, so one can step through the examples in the text. Actually playing with these techniques is crucial for getting a feel for how they work and how to use them and this is probably as close as the authors can get to bringing that experience to the reader.

If you know absolutely nothing about Monte Carlo methods, this book will give you a taste of what they are and what they can be used for. It is a pretty short text and I would consider it fairly light reading so it should be worth the time investment. If you think that you probably need an in-depth knowledge of these methods, MCSM is probably a better use of time (it is also pretty easy reading for a math book).
3 von 3 Kunden fanden die folgende Rezension hilfreich
2.0 von 5 Sternen decent material but disastrous editing 25. Juli 2013
Von lognorm - Veröffentlicht auf Amazon.com
Format:Taschenbuch|Verifizierter Kauf
the material itself is decent. the explanation is easy to follow and quite clear. it may not be very straightforward if you're new to r language or statistical concept in general, but still a well written. big problem is the reckless use of images. Somehow the publisher decided to use very low quality compressed image files so the figure labels are annoyingly blurry to read. with the capacities of R graphics, i believe this is a fairly simple matter to fix. i understand the focus of the book is not making fancy figures but they should have paid more attention to the quality of the book in general.
1 von 4 Kunden fanden die folgende Rezension hilfreich
3.0 von 5 Sternen It is an OK book if you already know MCMC. 10. August 2013
Von N. Vadulam - Veröffentlicht auf Amazon.com
Format:Taschenbuch|Verifizierter Kauf
This is just a run of the mill book that does not give the theory behind the methods.
The authors previous book on Monte carlo methods coauthored with casella is so theoretical, it is not understandable unless you already know the stuff.

None of these books is suitable for self study.
Waren diese Rezensionen hilfreich?   Wir wollen von Ihnen hören.

Kunden diskutieren

Das Forum zu diesem Produkt
Diskussion Antworten Jüngster Beitrag
Noch keine Diskussionen

Fragen stellen, Meinungen austauschen, Einblicke gewinnen
Neue Diskussion starten
Thema:
Erster Beitrag:
Eingabe des Log-ins
 

Kundendiskussionen durchsuchen
Alle Amazon-Diskussionen durchsuchen
   


Ähnliche Artikel finden