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Strong Limit Theorems For Large And Small Increments Of Ell P Valued Gaussian Processes
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Book Synopsis Strong Limit Theorems for Large and Small Increments of $ \ell ^ P $ -valued Gaussian Processes by : Miklós Csörgö
Download or read book Strong Limit Theorems for Large and Small Increments of $ \ell ^ P $ -valued Gaussian Processes written by Miklós Csörgö and published by . This book was released on 1991 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Strong Limit Theorems for Large and Small Increments of [ell Superscript P]-valued Gaussian Processes by : M. (Miklós) Csörg̋o
Download or read book Strong Limit Theorems for Large and Small Increments of [ell Superscript P]-valued Gaussian Processes written by M. (Miklós) Csörg̋o and published by . This book was released on 1991 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Strong Limit Theorems for Large and Small Increments of LP-valued Gaussian Processes by : Qi-Man Shao
Download or read book Strong Limit Theorems for Large and Small Increments of LP-valued Gaussian Processes written by Qi-Man Shao and published by . This book was released on 1991 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author :Carleton University. Laboratory for Research in Statistics and Probability Publisher : ISBN 13 : Total Pages : pages Book Rating :4.:/5 (13 download)
Book Synopsis Technical Report 180. Strong Limit Theorems for Large and Small Increments of -valued Gaussian Processes by : Carleton University. Laboratory for Research in Statistics and Probability
Download or read book Technical Report 180. Strong Limit Theorems for Large and Small Increments of -valued Gaussian Processes written by Carleton University. Laboratory for Research in Statistics and Probability and published by . This book was released on 1991 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Strong Limit Theorems by : Lin Zhengyan
Download or read book Strong Limit Theorems written by Lin Zhengyan and published by Springer Science & Business Media. This book was released on 2013-04-17 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents an up-to-date review of the most significant developments in strong Approximation and strong convergence in probability theory. The book consists of three chapters. The first deals with Wiener and Gaussian processes. Chapter 2 is devoted to the increments of partial sums of independent random variables. Chapter 3 concentrates on the strong laws of processes generated by infinite-dimensional Ornstein-Uhlenbeck processes. For researchers whose work involves probability theory and statistics.
Book Synopsis Uniform Central Limit Theorems by : R. M. Dudley
Download or read book Uniform Central Limit Theorems written by R. M. Dudley and published by Cambridge University Press. This book was released on 2014-02-24 with total page 485 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this new edition of a classic work on empirical processes the author, an acknowledged expert, gives a thorough treatment of the subject with the addition of several proved theorems not included in the first edition, including the Bretagnolle–Massart theorem giving constants in the Komlos–Major–Tusnady rate of convergence for the classical empirical process, Massart's form of the Dvoretzky–Kiefer–Wolfowitz inequality with precise constant, Talagrand's generic chaining approach to boundedness of Gaussian processes, a characterization of uniform Glivenko–Cantelli classes of functions, Giné and Zinn's characterization of uniform Donsker classes, and the Bousquet–Koltchinskii–Panchenko theorem that the convex hull of a uniform Donsker class is uniform Donsker. The book will be an essential reference for mathematicians working in infinite-dimensional central limit theorems, mathematical statisticians, and computer scientists working in computer learning theory. Problems are included at the end of each chapter so the book can also be used as an advanced text.
Book Synopsis Lectures on Gaussian Processes by : Mikhail Lifshits
Download or read book Lectures on Gaussian Processes written by Mikhail Lifshits and published by Springer Science & Business Media. This book was released on 2012-01-11 with total page 129 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gaussian processes can be viewed as a far-reaching infinite-dimensional extension of classical normal random variables. Their theory presents a powerful range of tools for probabilistic modelling in various academic and technical domains such as Statistics, Forecasting, Finance, Information Transmission, Machine Learning - to mention just a few. The objective of these Briefs is to present a quick and condensed treatment of the core theory that a reader must understand in order to make his own independent contributions. The primary intended readership are PhD/Masters students and researchers working in pure or applied mathematics. The first chapters introduce essentials of the classical theory of Gaussian processes and measures with the core notions of reproducing kernel, integral representation, isoperimetric property, large deviation principle. The brevity being a priority for teaching and learning purposes, certain technical details and proofs are omitted. The later chapters touch important recent issues not sufficiently reflected in the literature, such as small deviations, expansions, and quantization of processes. In university teaching, one can build a one-semester advanced course upon these Briefs.
Book Synopsis Small Ball Estimates for Gaussian Measures with Applications to Strong Limit Theorems by : Wenbo Li
Download or read book Small Ball Estimates for Gaussian Measures with Applications to Strong Limit Theorems written by Wenbo Li and published by . This book was released on 1992 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Large Deviations For Performance Analysis by : Adam Shwartz
Download or read book Large Deviations For Performance Analysis written by Adam Shwartz and published by CRC Press. This book was released on 1995-09-01 with total page 576 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book consists of two synergistic parts. The first half develops the theory of large deviations from the beginning (iid random variables) through recent results on the theory for processes with boundaries, keeping to a very narrow path: continuous-time, discrete-state processes. By developing only what is needed for the applications, the theory is kept to a manageable level, both in terms of length and in terms of difficulty. Within its scope, the treatment is detailed, comprehensive and self-contained. As the book shows, there are sufficiently many interesting applications of jump Markov processes to warrant a special treatment. The second half is a collection of applications developed at Bell Laboratories. The applications cover large areas of the theory of communication networks: circuit-switched transmission, packet transmission, multiple access channels, and the M/M/1 queue. Aspects of parallel computation are covered as well: basics of job allocation, rollback-based parallel simulation, assorted priority queueing models that might be used in performance models of various computer architectures, and asymptotic coupling of processors. These applications are thoroughly analyzed using the tools developed in the first half of the book. Features: A transient analysis of the M/M/1 queue; a new analysis of an Aloha model using Markov modulated theory; new results for Erlang's model; new results for the AMS model; analysis of "serve the longer queue", "join the shorter queue" and other simple priority queues; and a simple analysis of the Flatto-Hahn-Wright model of processor-sharing.
Book Synopsis Probability Theory and Stochastic Processes with Applications (Second Edition) by : Oliver Knill
Download or read book Probability Theory and Stochastic Processes with Applications (Second Edition) written by Oliver Knill and published by World Scientific Publishing Company. This book was released on 2017-01-31 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: This second edition has a unique approach that provides a broad and wide introduction into the fascinating area of probability theory. It starts on a fast track with the treatment of probability theory and stochastic processes by providing short proofs. The last chapter is unique as it features a wide range of applications in other fields like Vlasov dynamics of fluids, statistics of circular data, singular continuous random variables, Diophantine equations, percolation theory, random Schrödinger operators, spectral graph theory, integral geometry, computer vision, and processes with high risk.Many of these areas are under active investigation and this volume is highly suited for ambitious undergraduate students, graduate students and researchers.
Book Synopsis Interstellar Turbulence by : José Franco
Download or read book Interstellar Turbulence written by José Franco and published by Cambridge University Press. This book was released on 1999-05-28 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: This timely volume presents a series of review articles covering every aspect of interstellar turbulence--from accretion disks, molecular clouds, atomic and ionized media, through to spiral galaxies - based on a major international conference held in Mexico City.With advances in observational techniques and the development of more efficient computer codes and faster computers, research in this area has made spectacular progress in recent years. This book provides a comprehensive overview of the most important developments in observing and modelling turbulent flows in the cosmos. It provides graduate student and researchers with a state-of-the-art summary of observational, theoretical and computational research in interstellar turbulence.
Book Synopsis Optimization by Vector Space Methods by : David G. Luenberger
Download or read book Optimization by Vector Space Methods written by David G. Luenberger and published by John Wiley & Sons. This book was released on 1997-01-23 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: Engineers must make decisions regarding the distribution of expensive resources in a manner that will be economically beneficial. This problem can be realistically formulated and logically analyzed with optimization theory. This book shows engineers how to use optimization theory to solve complex problems. Unifies the large field of optimization with a few geometric principles. Covers functional analysis with a minimum of mathematics. Contains problems that relate to the applications in the book.
Book Synopsis Long Range Dependence by : Gennady Samorodnitsky
Download or read book Long Range Dependence written by Gennady Samorodnitsky and published by Now Publishers Inc. This book was released on 2007 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt: Long Range Dependence is a wide ranging survey of the ideas, models and techniques associated with the notion of long memory. It will serve as an invaluable reference source for researchers studying long range dependence, for those building long memory models, and for people who are trying to detect the possible presence of long memory in data.
Download or read book Surrogates written by Robert B. Gramacy and published by CRC Press. This book was released on 2020-03-10 with total page 560 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Gaussian Processes for Machine Learning by : Carl Edward Rasmussen
Download or read book Gaussian Processes for Machine Learning written by Carl Edward Rasmussen and published by MIT Press. This book was released on 2005-11-23 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Iterative Methods for Sparse Linear Systems by : Yousef Saad
Download or read book Iterative Methods for Sparse Linear Systems written by Yousef Saad and published by SIAM. This book was released on 2003-04-01 with total page 537 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematics of Computing -- General.
Book Synopsis The Elements of Statistical Learning by : Trevor Hastie
Download or read book The Elements of Statistical Learning written by Trevor Hastie and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 545 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.
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