Stochastic Analysis for Gaussian Random Processes and Fields
With Applications
Published on 23. June 2015
201 pages
978-1-4987-0782-4 (ISBN)
Description
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
More details
Series
Language
English
Place of publication
Philadelphia, PA
United States
Publishing group
Taylor & Francis Inc
Target group
College/higher education
File size
2,26 MB
ISBN-13
978-1-4987-0782-4 (9781498707824)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Vidyadhar S. Mandrekar | Leszek Gawarecki
Stochastic Analysis for Gaussian Random Processes and Fields
With Applications
Book
12/2020
1st Edition
Chapman & Hall/CRC
€59.41
Shipment within 15-20 days
Vidyadhar S. Mandrekar | Leszek Gawarecki
Stochastic Analysis for Gaussian Random Processes and Fields
With Applications
Book
06/2015
1st Edition
Chapman & Hall/CRC
€144.80
Shipment within 15-20 days
Persons
Vidyadhar Mandrekar is a professor in the Department of Statistics and Probability at Michigan State University. He earned a PhD in statistics from Michigan State University. His research interests include stochastic partial differential equations, stationary and Markov fields, stochastic stability, and signal analysis.
Leszek Gawarecki is head of the Department of Mathematics at Kettering University. He earned a PhD in statistics from Michigan State University. His research interests include stochastic analysis and stochastic ordinary and partial differential equations.
Leszek Gawarecki is head of the Department of Mathematics at Kettering University. He earned a PhD in statistics from Michigan State University. His research interests include stochastic analysis and stochastic ordinary and partial differential equations.
Content
Covariances and Associated Reproducing Kernel Hilbert Spaces. Gaussian Random Fields. Stochastic Integration for Gaussian Random Fields. Skorokhod and Malliavin Derivatives for Gaussian Random Fields. Filtering with General Gaussian Noise. Equivalence and Singularity. Markov Property of Gaussian Fields. Markov Property of Gaussian Fields and Dirichlet Forms. Bibliography. Index.
