Name: Stochastic Analysis for Gaussian Random Processes and Fields: With Applications (en Inglés)
SKU: 56035555
Price: 172.24 USD
Availability: InStock
Author: Mandrekar Vidyadhar S.; Gawarecki Leszek

portada Stochastic Analysis for Gaussian Random Processes and Fields: With Applications (en Inglés)

Stochastic Analysis for Gaussian Random Processes and Fields: With Applications (en Inglés)

Formato

Libro Físico

Editorial

Crc Pr Inc

Autor

Mandrekar Vidyadhar S.; Gawarecki Leszek

Idioma

Inglés

N° páginas

202

Encuadernación

Tapa Dura

ISBN13

9781498707817

N° edición

Stochastic Analysis for Gaussian Random Processes and Fields: With Applications (en Inglés)

Mandrekar Vidyadhar S.; Gawarecki Leszek (Autor) · Crc Pr Inc · Tapa Dura

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Reseña del libro "Stochastic Analysis for Gaussian Random Processes and Fields: With Applications (en Inglés)"

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 Itô 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.