Computational statistics handbook with MATLAB / Wendy L. Martinez, Angel R. Martinez.
By: Martinez, Wendy L.
Contributor(s): Martinez, Angel R.Material type: TextSeries: Series in computer science and data analysis: Publisher: Boca Raton, FL : Chapman & Hall/CRC, c2008Edition: 2nd ed.Description: xxiii, 767 p. : ill. ; 25 cm.ISBN: 9781584885665 (alk. paper); 1584885661 (alk. paper).Subject(s): Mathematical statistics -- Data processingDDC classification: 519.50285 Online resources: Table of contents only | WorldCat Details | Publisher description | E-book Fulltext
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|E-Book||EWU Library E-book||Non-fiction||519.50285 MAC (Browse shelf)||Not For Loan|
|Text||EWU Library Reserve Section||Non-fiction||519.50285 MAC 2008 (Browse shelf)||C-1||Not For Loan||25779|
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Includes bibliographical references (p. 731-750) and indexes.
Table of contents Introduction --
Probability concepts --
Sampling concepts --
Generating random variables --
Exploratory data analysis --
Finding structure --
Monte Carlo methods for inferential statistics --
Data partitioning --
Probability density estimation --
Supervised learning --
Unsupervised learning --
Parametric models --
Nonparametric models --
Markov chain Monte Carlo methods --
Spatial statistics --
Appendix A : Introduction to MATLAB® --
Appendix B : Projection pursuit indexes --
Appendix C : MATLAB® statistics toolbox --
Appendix D : Computational statistics toolbox --
Appendix E : Exploratory data analysis toolboxes --
Appendix F : Data sets --
Appendix G : Notation.
As with the bestselling first edition, "Computational Statistics Handbook with MATLAB" makes computational statistics as accessible as possible by playing down theory and building an understanding of the algorithms used in a wide range of applications. This second edition recognizes the new functionality of the MATLAB[registered] statistics toolbox and other updates like the new plotting capabilities of version 7.0. It includes a discussion of GUI functionality and expanded coverage of topics such as support vector machines, bagging, boosting, and random forests in pattern recognition. The text also features updated and new problem sets, data sets, exercises, and an appendix containing answers to selected problems.