Stochastic Dynamics, Filtering and Optimization
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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.Read more
- 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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- Date Published: January 2018
- format: Adobe eBook Reader
- isbn: 9781316996195
- availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
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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