Gaussian Random Processes - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Gaussian Random Processes write by I.A. Ibragimov. This book was released on 2012-12-06. Gaussian Random Processes available in PDF, EPUB and Kindle. The book deals mainly with three problems involving Gaussian stationary processes. The first problem consists of clarifying the conditions for mutual absolute continuity (equivalence) of probability distributions of a "random process segment" and of finding effective formulas for densities of the equiva lent distributions. Our second problem is to describe the classes of spectral measures corresponding in some sense to regular stationary processes (in par ticular, satisfying the well-known "strong mixing condition") as well as to describe the subclasses associated with "mixing rate". The third problem involves estimation of an unknown mean value of a random process, this random process being stationary except for its mean, i. e. , it is the problem of "distinguishing a signal from stationary noise". Furthermore, we give here auxiliary information (on distributions in Hilbert spaces, properties of sam ple functions, theorems on functions of a complex variable, etc. ). Since 1958 many mathematicians have studied the problem of equivalence of various infinite-dimensional Gaussian distributions (detailed and sys tematic presentation of the basic results can be found, for instance, in [23]). In this book we have considered Gaussian stationary processes and arrived, we believe, at rather definite solutions. The second problem mentioned above is closely related with problems involving ergodic theory of Gaussian dynamic systems as well as prediction theory of stationary processes.
Gaussian Processes for Machine Learning
Gaussian Processes for Machine Learning - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Gaussian Processes for Machine Learning write by Carl Edward Rasmussen. This book was released on 2005-11-23. Gaussian Processes for Machine Learning available in PDF, EPUB and Kindle. 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.
Stable Non-Gaussian Random Processes
Stable Non-Gaussian Random Processes - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Stable Non-Gaussian Random Processes write by Gennady Samoradnitsky. This book was released on 2017-11-22. Stable Non-Gaussian Random Processes available in PDF, EPUB and Kindle. 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.
Gaussian Random Functions
Gaussian Random Functions - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Gaussian Random Functions write by M.A. Lifshits. This book was released on 2013-03-09. Gaussian Random Functions available in PDF, EPUB and Kindle. It is well known that the normal distribution is the most pleasant, one can even say, an exemplary object in the probability theory. It combines almost all conceivable nice properties that a distribution may ever have: symmetry, stability, indecomposability, a regular tail behavior, etc. Gaussian measures (the distributions of Gaussian random functions), as infinite-dimensional analogues of tht
Introduction to Random Processes
Introduction to Random Processes - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Introduction to Random Processes write by E. Wong. This book was released on 2013-03-09. Introduction to Random Processes available in PDF, EPUB and Kindle.
