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 before introducing the Gaussian
process and Gaussian random fields. The authors use chaos expansion
to define the Skorokhod integral, which generalizes the Ito
integral. They show how the Skorokhod integral is a dual operator
of Skorokhod differentiation and the divergence operator of
Malliavin. The authors also present Gaussian processes indexed by
real numbers and obtain a Kallianpur-Striebel Bayes' formula for
the filtering problem. After discussing the problem of equivalence
and singularity of Gaussian random fields (including a
generalization of the Girsanov theorem), the book concludes with
the Markov property of Gaussian random fields indexed by measures
and generalized Gaussian random fields indexed by Schwartz space.
The Markov property for generalized random fields is connected to
the Markov process generated by a Dirichlet form.
General
Is the information for this product incomplete, wrong or inappropriate?
Let us know about it.
Does this product have an incorrect or missing image?
Send us a new image.
Is this product missing categories?
Add more categories.
Review This Product
No reviews yet - be the first to create one!
Welcome to Loot.co.za!
Sign in / Register |Wishlists & Gift Vouchers |Help | Advanced search
