Stochastic dynamics, filtering, and optimization / Debasish Roy, G. Visweswara Rao.
By: Roy, Debasish Kumar [author.]
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Contributor(s): G., Visweswara Rao (Gorti) [author.].
Material type: 
BookPublisher: Cambridge : Cambridge University Press, 2017Description: 1 online resource (xxxvii, 709 pages) : digital, PDF file(s).Content type: text Media type: computer Carrier type: online resourceISBN: 9781316863107 (ebook).Subject(s): Random variables -- Textbooks | Probabilities -- Textbooks | Stochastic processes -- TextbooksAdditional physical formats: Print version: : No titleDDC classification: 519.2 Online resources: Click here to access online Summary: Targeted at graduate students, researchers and practitioners in the field of science and engineering, this book gives a self-contained introduction to a measure-theoretic framework in laying out the definitions and basic concepts of random variables and stochastic diffusion processes. It then continues to weave into a framework of several practical tools and applications involving stochastic dynamical systems. These include tools for the numerical integration of such dynamical systems, nonlinear stochastic filtering and generalized Bayesian update theories for solving inverse problems and a new stochastic search technique for treating a broad class of non-convex optimization problems. MATLAB® codes for all the applications are uploaded on the companion website.
Title from publisher's bibliographic system (viewed on 12 Feb 2018).
Targeted at graduate students, researchers and practitioners in the field of science and engineering, this book gives a self-contained introduction to a measure-theoretic framework in laying out the definitions and basic concepts of random variables and stochastic diffusion processes. It then continues to weave into a framework of several practical tools and applications involving stochastic dynamical systems. These include tools for the numerical integration of such dynamical systems, nonlinear stochastic filtering and generalized Bayesian update theories for solving inverse problems and a new stochastic search technique for treating a broad class of non-convex optimization problems. MATLAB® codes for all the applications are uploaded on the companion website.
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