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Author: Mircea Grigoriu
Publisher: Prentice Hall
Published: 1995
Total Pages: 472
ISBN-13:
DOWNLOAD EBOOK →This text defines a variety of non-Gaussian processes, develops methods for generating realizations of non-Gaussian models, and provides methods for finding probabilistic characteristics of the output of linear filters with non-Gaussian inputs.
Author: Edward J. Wegman
Publisher: Springer Science & Business Media
Published: 2012-12-06
Total Pages: 246
ISBN-13: 1461388597
DOWNLOAD EBOOK →Non-Gaussian Signal Processing is a child of a technological push. It is evident that we are moving from an era of simple signal processing with relatively primitive electronic cir cuits to one in which digital processing systems, in a combined hardware-software configura. tion, are quite capable of implementing advanced mathematical and statistical procedures. Moreover, as these processing techniques become more sophisticated and powerful, the sharper resolution of the resulting system brings into question the classic distributional assumptions of Gaussianity for both noise and signal processes. This in turn opens the door to a fundamental reexamination of structure and inference methods for non-Gaussian sto chastic processes together with the application of such processes as models in the context of filtering, estimation, detection and signal extraction. Based on the premise that such a fun damental reexamination was timely, in 1981 the Office of Naval Research initiated a research effort in Non-Gaussian Signal Processing under the Selected Research Opportunities Program.
Author: Carl Edward Rasmussen
Publisher: MIT Press
Published: 2005-11-23
Total Pages: 266
ISBN-13: 026218253X
DOWNLOAD EBOOK →A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.
Author: Gennady Samoradnitsky
Publisher: Routledge
Published: 2017-11-22
Total Pages: 632
ISBN-13: 1351414801
DOWNLOAD EBOOK →This book serves as a standard reference, making this area accessible not only to researchers in probability and statistics, but also to graduate students and practitioners. The book assumes only a first-year graduate course in probability. Each chapter begins with a brief overview and concludes with a wide range of exercises at varying levels of difficulty. The authors supply detailed hints for the more challenging problems, and cover many advances made in recent years.
Author: Gennady Samorodnitsky
Publisher: CRC Press
Published: 1994-06-01
Total Pages: 662
ISBN-13: 9780412051715
DOWNLOAD EBOOK →Both an introduction and a basic reference text on non-Gaussian stable models, for graduate students and practitioners. Assuming only a first-year graduate course in probability, it includes material which has only recently appeared in journals and unpublished materials. Each chapter begins with a brief overview and concludes with a range of exercises at varying levels of difficulty. Proofs are spelled out in detail. The volume includes a discussion of self-similar processes, ARMA, and fractional ARIMA time series with stable innovations. Annotation copyright by Book News, Inc., Portland, OR
Author: P.D. Spanos
Publisher: CRC Press
Published: 1999-11-09
Total Pages: 628
ISBN-13: 9789058090393
DOWNLOAD EBOOK →Proceedings of the June, 1998 conference. Seventy contributions discuss Monte Carlo and signal processing methods, random vibrations, safety and reliability, control/optimization and modeling of nonlinearity, earthquake engineering, random processes and fields, damage/fatigue materials, applied prob
Author: Murray Rosenblatt
Publisher: Springer Science & Business Media
Published: 2012-12-06
Total Pages: 252
ISBN-13: 1461212626
DOWNLOAD EBOOK →The principal focus here is on autoregressive moving average models and analogous random fields, with probabilistic and statistical questions also being discussed. The book contrasts Gaussian models with noncausal or noninvertible (nonminimum phase) non-Gaussian models and deals with problems of prediction and estimation. New results for nonminimum phase non-Gaussian processes are exposited and open questions are noted. Intended as a text for gradutes in statistics, mathematics, engineering, the natural sciences and economics, the only recommendation is an initial background in probability theory and statistics. Notes on background, history and open problems are given at the end of the book.
Author: Robert B. Gramacy
Publisher: CRC Press
Published: 2020-03-10
Total Pages: 560
ISBN-13: 1000766209
DOWNLOAD EBOOK →Computer simulation experiments are essential to modern scientific discovery, whether that be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are meta-models of computer simulations, used to solve mathematical models that are too intricate to be worked by hand. Gaussian process (GP) regression is a supremely flexible tool for the analysis of computer simulation experiments. This book presents an applied introduction to GP regression for modelling and optimization of computer simulation experiments. Features: • Emphasis on methods, applications, and reproducibility. • R code is integrated throughout for application of the methods. • Includes more than 200 full colour figures. • Includes many exercises to supplement understanding, with separate solutions available from the author. • Supported by a website with full code available to reproduce all methods and examples. The book is primarily designed as a textbook for postgraduate students studying GP regression from mathematics, statistics, computer science, and engineering. Given the breadth of examples, it could also be used by researchers from these fields, as well as from economics, life science, social science, etc.
Author: Jian Qing Shi
Publisher: CRC Press
Published: 2011-07-01
Total Pages: 214
ISBN-13: 1439837740
DOWNLOAD EBOOK →Gaussian Process Regression Analysis for Functional Data presents nonparametric statistical methods for functional regression analysis, specifically the methods based on a Gaussian process prior in a functional space. The authors focus on problems involving functional response variables and mixed covariates of functional and scalar variables.Coveri
