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Stochastic analysis for Gaussian random processes and fields : with applications / Vidyadhar S. Mandrekar and Leszek Gawarecki.

By: Mandrekar, Vidyadhar S [author].
Contributor(s): Gawarecki, Leszek [author].
Material type: TextTextSeries: Monographs on statistics and applied probability ; 145.Publisher: Boca Raton : CRC Press, ©2016Description: xxi, 179 p. ; 24 cm.ISBN: 9781498707817 .Subject(s): Stochastic processes | Gaussian processesDDC classification: 519.23
Contents:
Chapter 1: Covariances and Associated Reproducing Kernel Hilbert Spaces; Chapter 2: Gaussian Random Fields; Chapter 3: Stochastic Integration for Gaussian Random Fields; Chapter 4: Skorokhod and Malliavin Derivatives for Gaussian Random Fields; Chapter 5: Filtering with General Gaussian Noise; Chapter 6: Equivalence and Singularity; Chapter 7: Markov Property of Gaussian Fields; Chapter 8: Markov Property of Gaussian Fields and Dirichlet Forms.
Summary: 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 differenti.
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Item type Current location Call number Status Date due Barcode Item holds
Books Books ISI Library, Kolkata
 
519.23 M273 (Browse shelf) Available 137132
Total holds: 0

Includes bibliographical references and index.

Chapter 1: Covariances and Associated Reproducing Kernel Hilbert Spaces;
Chapter 2: Gaussian Random Fields;
Chapter 3: Stochastic Integration for Gaussian Random Fields; Chapter 4: Skorokhod and Malliavin Derivatives for Gaussian Random Fields;
Chapter 5: Filtering with General Gaussian Noise;
Chapter 6: Equivalence and Singularity;
Chapter 7: Markov Property of Gaussian Fields;
Chapter 8: Markov Property of Gaussian Fields and Dirichlet Forms.

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 differenti.

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