"Provides a self-contained introduction to a measure-theoretic framework in laying out the definitions and basic concepts of random variables and stochastic diffusion processes"--
"Provides a self-contained introduction to a measure-theoretic framework in laying out the definitions and basic concepts of random variables and stochastic diffusion processes"--Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
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.
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