Permutation, Parametric, and Bootstrap Tests of Hypotheses (Springer Series in Statistics)
Springer | ISBN 038720279X | 2004-12-01 | PDF | 376 pages | 2.31 MB
This book provides a step-by-step manual on the application of permutation tests in biology, business, medicine, science, and engineering. Its intuitive and informal style will ideally suit it as a text for students and researchers whether experienced or coming to these resampling methods for the first time. The real-world problems of missing and censored data, multiple comparisons, nonresponders, after-the-fact covariates, and outliers are dealt with at length.
The book’s main features include:
* detailed consideration of one-, two-, and k-sample tests, contingency tables, experimental design, clinical trials, cluster analysis, multiple comparisons, multivariate data, regression, and sample size reduction;
* numerous practical applications in archeology, biology, climatology, economics, education, medicine, and the social sciences;
* valuable techniques for reducing computation time;
* practical advice on experimental design;
* comparisons with bootstrap, parametric, and nonparametric techniques;
* an extensive three-part bibliography featuring more than 1,000 articles.
This new edition has more than 100 additional pages, and includes streamlined statistics for the k-sample comparison and analysis of variance plus expanded sections on computational techniques, multiple comparisons, multiple regression, comparing variances, and testing interactions in balanced designs. Comprehensive author and subject indexes, plus an expert-system guide to methods, provide for further ease of use. The invaluable exercises at the end of every chapter have been supplemented with drills and a number of graduate-level thesis problems.
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Simon S. Haykin, “Kalman Filtering and Neural Networks”
Wiley InterScience | ISBN 0471221546 (electronic) | 2001 | 284 pages | PDF | 4 MB RAR’ed
Book Description
State-of-the-art coverage of Kalman filter methods for the design of neural networks
This self-contained book consists of seven chapters by expert contributors that discuss Kalman filtering as applied to the training and use of neural networks. Although the traditional approach to the subject is almost always linear, this book recognizes and deals with the fact that real problems are most often nonlinear.
The first chapter offers an introductory treatment of Kalman filters with an emphasis on basic Kalman filter theory, Rauch-Tung-Striebel smoother, and the extended Kalman filter. Other chapters cover:
* An algorithm for the training of feedforward and recurrent multilayered perceptrons, based on the decoupled extended Kalman filter (DEKF)
* Applications of the DEKF learning algorithm to the study of image sequences and the dynamic reconstruction of chaotic processes
* The dual estimation problem
* Stochastic nonlinear dynamics: the expectation-maximization (EM) algorithm and the extended Kalman smoothing (EKS) algorithm
* The unscented Kalman filter
Each chapter, with the exception of the introduction, includes illustrative applications of the learning algorithms described here, some of which involve the use of simulated and real-life data. Kalman Filtering and Neural Networks serves as an expert resource for researchers in neural networks and nonlinear dynamical systems.
An Instructor’s Manual presenting detailed solutions to all the problems in the book is available upon request from the Wiley Makerting Department.
Review
“Although the traditional approach to the subject is usually linear, this book recognizes and deals with the fact that real problems are most often nonlinear.” (SciTech Book News, Vol. 25, No. 4, December 2001)
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Relativity: The Special and the General Theory, The Masterpiece Science Edition
Pi Press | ISBN 0131862618| 2005-04-05 | PDF | 288 pages | 1.69 MB
From the Commentary by Robert Geroch (The corresponding section of Einstein’s text can be found below the comment. Please note that in the book, the Commentary is placed after the complete text of Relativity.)
Section 17. Space-Time
Minkowski’s viewpoint represents a “geometrization” of relativity. These ideas have, over the years, come to the forefront: They reflect the perspective of the majority of physicists working in relativity today. Let us expand on this viewpoint. The fundamental notion is that of an event, which we think of as a physical occurrence having negligibly small extension in both space and time. That is, an event is “small and quick,” such as the explosion of a firecracker or the snapping of your fingers. Now consider the collection of all possible events in the universe—all events that have ever happened, all that are happening now, and all that will ever happen; here and elsewhere. This collection is called space-time. It is the arena in which physics takes place in relativity. The idea is to recast all statements about goings-on in the physical world into geometrical structures within this space-time. In a similar vein, you might begin the study of plane geometry by introducing the notion of a point (analogous to an event) and assembling all possible points into the plane (analogous to space-time). This plane is the arena for plane geometry, and each statement that is part of plane geometry is to be cast as geometrical structure within this plane.
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