Posted by **ChrisRedfield** at Oct. 1, 2013

Published: 2013-02-23 | ISBN: 3540746994 | PDF | 310 pages | 12 MB

Posted by **nebulae** at Nov. 26, 2016

English | ISBN: 0759123403, 0759123411 | 2013 | 255 pages | PDF | 5 MB

Posted by **tanas.olesya** at Sept. 21, 2016

English | 24 May 2006 | ISBN: 0387249753 | 688 Pages | PDF | 3 MB

Optimization Theory and Methods can be used as a textbook for an optimization course for graduates and senior undergraduates.

Posted by **leonardo78** at Jan. 8, 2016

Publisher: Narosa | 2000 | ISBN: 0849309808 | 144 pages | PDF | 3,77 MB

Sampling Theory and Methods presents the theoretical aspects of "Sample Surveys" in a lucid form for the benefit of both undergraduate and post graduate students of Statistics. It assumes very little background in probability theory.

Posted by **fdts** at Jan. 4, 2016

by Vladimir Savchuk (Author), Chris P. Tsokos

English | 2011 | ISBN: 9491216139 | 318 pages | PDF | 3.75 MB

Posted by **BUGSY** at May 21, 2015

English | June 5, 2007 | ISBN: 0470015624 | 539 Pages | PDF | 3 MB

Bayesian methods combine information available from data with any prior information available from expert knowledge. The Bayes linear approach follows this path, offering a quantitative structure for expressing beliefs, and systematic methods for adjusting these beliefs, given observational data.

Posted by **interes** at Feb. 1, 2015

English | 2006-08-28 | ISBN: 0387400842 | PDF | 352 pages | 4,6 MB

Posted by **tanas.olesya** at Jan. 19, 2015

English | Mar 30, 2007 | ISBN: 0470114029 | 307 Pages | PDF | 9 MB

Long-Memory Time Series: Theory and Methods provides an overview of the theory and methods developed to deal with long-range dependent data and describes the applications of these methodologies to real-life time series.

Posted by **lenami** at Sept. 4, 2012

ISBN: 0471681822 | edition 2007 | PDF | 557 pages | 4 mb

An interdisciplinary framework for learning methodologies—covering statistics, neural networks, and fuzzy logic, this book provides a unified treatment of the principles and methods for learning dependencies from data. It establishes a general conceptual framework in which various learning methods from statistics, neural networks, and fuzzy logic can be applied—showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science. Complete with over one hundred illustrations, case studies, and examples making this an invaluable text.

Posted by **Specialselection** at April 29, 2012

English | 2006-05-24 | ISBN: 0387249753 | 689 pages | PDF | 3.7 mb