Stochastic Analysis for Gaussian Random Processes and Fields

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This monograph presents Hilbert space methods to study deep analytic properties connecting probabilistic notions. In particular, the authors study Gaussian random fields using reproducing kernel Hilbert spaces (RKHSs). They explain how covariances are related to RKHSs and examine the Bayes¿ formula, the filtering and analytic problem related to fractional Brownian motion, and equivalence and singularity of Gaussian random fields. The book also describes applications in finance and spatial statistics and presents results on Dirichlet forms and associated Markov processes.