Stochastic Analysis For Gaussian Random Processes And Fields
Download Stochastic Analysis For Gaussian Random Processes And Fields PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Stochastic Analysis For Gaussian Random Processes And Fields book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Stochastic Analysis for Gaussian Random Processes and Fields
Stochastic Analysis for Gaussian Random Processes and Fields: With Applications presents Hilbert space methods to study deep analytic properties connecting probabilistic notions. In particular, it studies Gaussian random fields using reproducing kernel Hilbert spaces (RKHSs).The book begins with preliminary results on covariance and associated RKHS
Statistics of Random Processes II
Author: Robert Shevilevich Lipt︠s︡er
language: en
Publisher: Springer Science & Business Media
Release Date: 2001
"Written by two renowned experts in the field, the books under review contain a thorough and insightful treatment of the fundamental underpinnings of various aspects of stochastic processes as well as a wide range of applications. Providing clear exposition, deep mathematical results, and superb technical representation, they are masterpieces of the subject of stochastic analysis and nonlinear filtering....These books...will become classics." --SIAM REVIEW
Random Fields and Geometry
Author: R. J. Adler
language: en
Publisher: Springer Science & Business Media
Release Date: 2009-01-29
This monograph is devoted to a completely new approach to geometric problems arising in the study of random fields. The groundbreaking material in Part III, for which the background is carefully prepared in Parts I and II, is of both theoretical and practical importance, and striking in the way in which problems arising in geometry and probability are beautifully intertwined. "Random Fields and Geometry" will be useful for probabilists and statisticians, and for theoretical and applied mathematicians who wish to learn about new relationships between geometry and probability. It will be helpful for graduate students in a classroom setting, or for self-study. Finally, this text will serve as a basic reference for all those interested in the companion volume of the applications of the theory.