Applied Stochastic Modelling
Morgan, Byron J.T.
Sold by Kennys Bookstore, Olney, MD, U.S.A.
AbeBooks Seller since 9 October 2009
New - Soft cover
Condition: New
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Add to basketSold by Kennys Bookstore, Olney, MD, U.S.A.
AbeBooks Seller since 9 October 2009
Condition: New
Quantity: 10 available
Add to basketCovers both contemporary and classical aspects of statistics, including survival analysis, Kernel density estimation, Markov chain Monte Carlo, hypothesis testing, regression, bootstrap, and generalised linear models. This work provides the option of using powerful computational tools for stochastic modelling. Series Editor(s): Zidek, James V.; Tanner, Martin Abba; Carlin, Bradley P. Series: Chapman & Hall/CRC Texts in Statistical Science. Num Pages: 368 pages, 73 black & white illustrations. BIC Classification: PBWL. Category: (P) Professional & Vocational. Dimension: 235 x 154 x 19. Weight in Grams: 520. . 2008. 2nd Edition. paperback. . . . . Books ship from the US and Ireland.
Seller Inventory # V9781584886662
Highlighting modern computational methods, Applied Stochastic Modelling, Second Edition provides students with the practical experience of scientific computing in applied statistics through a range of interesting real-world applications. It also successfully revises standard probability and statistical theory. Along with an updated bibliography and improved figures, this edition offers numerous updates throughout.
New to the Second Edition
The book covers both contemporary and classical aspects of statistics, including survival analysis, Kernel density estimation, Markov chain Monte Carlo, hypothesis testing, regression, bootstrap, and generalised linear models. Although the book can be used without reference to computational programs, the author provides the option of using powerful computational tools for stochastic modelling. All of the data sets and MATLAB® and R programs found in the text as well as lecture slides and other ancillary material are available for download at www.crcpress.com
Continuing in the bestselling tradition of its predecessor, this textbook remains an excellent resource for teaching students how to fit stochastic models to data.
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