We see that javascript is disabled or not supported by your browser - javascript is needed for important actions on the site. Read more

What's New - Home - Login

Member $avings: $81,567,077.52 | Books Available: 795,682 | Members Online: 184

Swap Used Books - Buy New Books at Great Prices!

How To Swap Books Sign Up Search submenu

PBS Market (New Books)

Gift Buying Guide

Book Browser Advanced Search Books Posted Today Member Book Reviews Award Winning Books NYT Best Sellers Amazon Best Sellers Most Traveled Copies Club Wish List

Discussion Forums Book Lists

Club Lists My Book Lists My Watched Lists Create a List

Blog Donations

School Donation Program In Memory of... Military Donation Program Friends of PBS

Box-O-Books Maps The Eclectic Pen Fun Stuff

20 Questions Sudoku Bookmark Creator

Top 100

Requests Posts Swappers Referrers Reviewers

Pulse of PBS Spread The Word

Invite Friends Bookmarks Facebook Page Facebook App More Ways...

Photo Gallery Recipes Club Tag Cloud Member Testimonials

Help Center submenu

How To Swap Books Browse Help Docs Ask the Librarian PBS Member Icons Live Help

Kiosk

PBS Market (New Books) Go Shopping Buy Credits Buy PBS Money Upgrade Membership Gift Certificates Transfer Credits

Need Help?

Search - Monte Carlo Statistical Methods (Springer Texts in Statistics)

Monte Carlo Statistical Methods - Springer Texts in Statistics
Author: Christian P. Robert, George Casella

Monte Carlo statistical methods, particularly those based on Markov chains, are now an essential component of the standard set of techniques used by statisticians. This new edition has been revised towards a coherent and flowing coverage of these simulation techniques, with incorporation of the most recent developments in the field. In particula... more »r, the introductory coverage of random variable generation has been totally revised, with many concepts being unified through a fundamental theorem of simulation There are five completely new chapters that cover Monte Carlo control, reversible jump, slice sampling, sequential Monte Carlo, and perfect sampling. There is a more in-depth coverage of Gibbs sampling, which is now contained in three consecutive chapters. The development of Gibbs sampling starts with slice sampling and its connection with the fundamental theorem of simulation, and builds up to two-stage Gibbs sampling and its theoretical properties. A third chapter covers the multi-stage Gibbs sampler and its variety of applications. Lastly, chapters from the previous edition have been revised towards easier access, with the examples getting more detailed coverage. This textbook is intended for a second year graduate course, but will also be useful to someone who either wants to apply simulation techniques for the resolution of practical problems or wishes to grasp the fundamental principles behind those methods. The authors do not assume familiarity with Monte Carlo techniques (such as random variable generation), with computer programming, or with any Markov chain theory (the necessary concepts are developed in Chapter 6). A solutions manual, which covers approximately 40% of the problems, is available for instructors who require the book for a course. Christian P. Robert is Professor of Statistics in the Applied Mathematics Department at Université Paris Dauphine, France. He is also Head of the Statistics Laboratory at the Center for Research in Economics and Statistics (CREST) of the National Institute for Statistics and Economic Studies (INSEE) in Paris, and Adjunct Professor at Ecole Polytechnique. He has written three other books, including The Bayesian Choice, Second Edition, Springer 2001. He also edited Discretization and MCMC Convergence Assessment, Springer 1998. He has served as associate editor for the Annals of Statistics and the Journal of the American Statistical Association. He is a fellow of the Institute of Mathematical Statistics, and a winner of the Young Statistician Award of the Societié de Statistique de Paris in 1995. George Casella is Distinguished Professor and Chair, Department of Statistics, University of Florida. He has served as the Theory and Methods Editor of the Journal of the American Statistical Association and Executive Editor of Statistical Science. He has authored three other textbooks: Statistical Inference, Second Edition, 2001, with Roger L. Berger; Theory of Point Estimation, 1998, with Erich Lehmann; and Variance Components, 1992, with Shayle R. Searle and Charles E. McCulloch. He is a fellow of the Institute of Mathematical Statistics and the American Statistical Association, and an elected fellow of the International Statistical Institute.

ISBN-13: 9780387212395
ISBN-10: 0387212396
Publication Date: 7/26/2005
Pages: 645
Rating:

0 stars, based on 0 rating

Publisher: Springer
Book Type: Hardcover
Other Versions: Paperback
Members Wishing: 1
Reviews: Amazon | Write a Review

Genres:

Computers & Technology >> Business Technology >> Project Management >> Statistical Computing
Science & Math >> Mathematics >> Applied >> Probability & Statistics
Engineering & Transportation >> Professional Science >> Mathematics >> Applied >> Statistics

Back to the Top ↑

Search - Monte Carlo Statistical Methods (Springer Texts in Statistics)

Loading...