Aimed at students and researchers in mathematics, communications, engineering and economics, this book describes the probabilistic structure of a Gaussian process in terms of its canonical representation (or its innovation process). Multiple Markov properties of a Gaussian process and equivalence problems of Gaussian processes are clearly presented. The authors' approach involves causality in time evolution and information-theoretic aspects. As the book is self-contained and only requires background in the fundamentals of ...
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Aimed at students and researchers in mathematics, communications, engineering and economics, this book describes the probabilistic structure of a Gaussian process in terms of its canonical representation (or its innovation process). Multiple Markov properties of a Gaussian process and equivalence problems of Gaussian processes are clearly presented. The authors' approach involves causality in time evolution and information-theoretic aspects. As the book is self-contained and only requires background in the fundamentals of probability theory and measure theory, it would be suitable as a textbook at the senior undergraduate or graduate level.
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