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Stochastic Dynamics, Filtering and Optimization

Stochastic Dynamics, Filtering and Optimization

$103.99 (C)

  • Date Published: May 2017
  • availability: Available
  • format: Hardback
  • isbn: 9781107182646

$ 103.99 (C)

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About the Authors
  • 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.

    • Applications to dynamical systems in science, engineering and recursive search algorithms are covered in depth
    • Important topics including Radon-Nikodym derivatives and Girsanov theorems with emphasis on Ito diffusion processes are discussed comprehensively
    • MATLAB® codes for all the applications are available on the companion website for both students and instructors
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    Product details

    • Date Published: May 2017
    • format: Hardback
    • isbn: 9781107182646
    • length: 742 pages
    • dimensions: 249 x 190 x 39 mm
    • weight: 1.32kg
    • availability: Available
  • Table of Contents

    List of figures
    List of tables
    1. Probability theory and random variables
    2. Random variables: conditioning, convergence and simulation
    3. An introduction to stochastic processes
    4. Stochastic calculus and diffusion processes
    5. Numerical solutions to stochastic differential equations
    6. Non-Linear Stochastic Filtering and Recursive Monte Carlo Estimation
    7. Nonlinear filters with gain-type additive updates
    8. Improved numerical solutions to SDEs by change of measure
    9. Evolutionary global optimization via change of measures: A Martingale Route
    10. COMBEO – a new global optimization scheme by change of measures

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    Stochastic Dynamics, Filtering and Optimization

    Debasish Roy, G. Visweswara Rao

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  • Authors

    Debasish Roy, Indian Institute of Science, Bangalore
    Debasish Roy is currently working as Professor in the Computational Mechanics Laboratory at the Indian Institute of Science, Bangalore. He obtained his Ph.D. from the Indian Institute of Science, followed by post-doctoral research at the University of Innsbruck, Austria. Besides being a fellow of the Indian National Academy of Engineering, he has also held an Honorary Professorship in the School of Engineering, University of Aberdeen, and a distinguished visiting fellowship of the Royal Academy of Engineering, London. His areas of research include computational mechanics of non-classical continua, stochastic dynamical systems and optimization/inverse problems. He has published over 120 papers in journals of international repute, delivered keynote/invited lectures at many international conferences and served on editorial boards.

    G. Visweswara Rao
    G. Visweswara Rao was Technical Advisor in ACS Design Consulting Private Limited, Bangalore. He received his Ph.D. from the Indian Institute of Science, Bangalore, in 1989. He has published several research papers in the areas of structural dynamics specific to earthquake engineering, nonlinear and random vibration, and structural control, and co-authored Elements of Structural Dynamics: A New Perspective (2012) with D. Roy. His areas of research include non-linear and stochastic structural dynamics.

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