Debasish Roy

Stochastic Dynamics, Filtering and Optimization (eBook, PDF)

• Format: PDF

Produktbeschreibung

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(R) codes for all the applications are uploaded on the companion website.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 4. Mai 2017
• Englisch
• ISBN-13: 9781316996195
• Artikelnr.: 48411201

Autorenporträt

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.

Inhaltsangabe

List of figures
List of tables
Preface
Dedication
Acronyms
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
Appendices
Bibliography
References.