Springer. Used - Good. Ships from UK in 48 hours or less (usually same day). Your purchase helps support Sri Lankan Children's Charity 'The Rainbow Centre'. Ex-library, so some stamps and wear, but in good overall condition. 100% money back guarantee. We are a world class secondhand bookstore based in Hertfordshire, United Kingdom and specialize in high quality textbooks across an enormous variety of subjects. We aim to provide a vast range of textbooks, rare and collectible books at a great price. Our donations to The Rainbow Centre have helped provide an education and a safe haven to hundreds of children who live in appalling conditions. We provide a 100% money back guarantee and are dedicated to providing our customers with the highest standards of service in the bookselling industry. Springer ISBN 0387922970 9780387922973 [GB]

Springer, Date: 2009-05-15. Paperback. Good. Textbook, May Have Highlights, Notes and/or Underlining, BOOK ONLYNO ACCESS CODE, NO CD, Ships with Emailed Tracking 2009. Springer ISBN 0387922970 9780387922973 [US]

ISBN10: 0387922970, ISBN13: 9780387922973, [publisher: Springer New York] Softcover Druck auf Anfrage Neuware - Printed after ordering - There has been dramatic growth in the development and application of Bayesian inference in statistics. Berger (2000) documents the increase in Bayesian activity by the number of published research articles, the number of books,andtheextensivenumberofapplicationsofBayesianarticlesinapplied disciplines such as science and engineering. One reason for the dramatic growth in Bayesian modeling is the availab- ity of computational algorithms to compute the range of integrals that are necessary in a Bayesian posterior analysis. Due to the speed of modern c- puters, it is now possible to use the Bayesian paradigm to t very complex models that cannot be t by alternative frequentist methods. To t Bayesian models, one needs a statistical computing environment. This environment should be such that one can: write short scripts to de ne a Bayesian model use or write functions to summarize a posterior distribution use functions to simulate from the posterior distribution construct graphs to illustrate the posterior inference An environment that meets these requirements is the R system. R provides a wide range of functions for data manipulation, calculation, and graphical d- plays. Moreover, it includes a well-developed, simple programming language that users can extend by adding new functions. Many such extensions of the language in the form of ...
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Springer Verlag, Date: 2009. Paperback. New. 2nd edition. 298 pages. 9.00x6.25x0.75 inches. 2009. Springer Verlag ISBN 0387922970 9780387922973 [GB]