Discrete fractional calculus : applications in control and image processing
By: Ostalczyk, Piotr
Material type:
BookSeries: Publisher: New Jersey : World Scientific, c2016; ©2016.Description: xxxi, 361 p. : ill. ; 26 cm.ISBN: 9789814725668Subject(s): Fractional calculus | Computer science -- MathematicsDDC classification: 515/.83 Online resources: Location Map | Item type | Home library | Call number | Status | Date due | Barcode | Item holds |
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| REGULAR | University of Wollongong in Dubai Main Collection | 515.83 OS DI (Browse shelf) | Available | T0011231 |
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| 515.7223 KO IN An introduction to the representation theory of groups | 515.83 FA FR Fractional calculus and fractional processes with applications to financial economics | 515.83 HE FR Fractional calculus : | 515.83 OS DI Discrete fractional calculus : | 515.88 KY EL Elements of concave analysis and applications | 515.9 CO RO The corona problem: | 516 MA AP Applied geometry for computer graphics and CAD / |
Includes bibliographical references (p. 341-358) and index.
Preface; 1. Introduction; Part I. Stochastic Models and Bayesian Filtering: 2. Stochastic state-space models; 3. Optimal filtering; 4. Algorithms for maximum likelihood parameter estimation; 5. Multi-agent sensing: social learning and data incest; Part II. Partially Observed Markov Decision Processes. Models and Algorithms: 6. Fully observed Markov decision processes; 7. Partially observed Markov decision processes (POMDPs); 8. POMDPs in controlled sensing and sensor scheduling; Part III. Partially Observed Markov Decision Processes: 9. Structural results for Markov decision processes; 10. Structural results for optimal filters; 11. Monotonicity of value function for POMPDs; 12. Structural results for stopping time POMPDs; 13. Stopping time POMPDs for quickest change detection; 14. Myopic policy bounds for POMPDs and sensitivity to model parameters; Part IV. Stochastic Approximation and Reinforcement Learning: 15. Stochastic optimization and gradient estimation; 16. Reinforcement learning; 17. Stochastic approximation algorithms: examples; 18. Summary of algorithms for solving POMPDs; Appendix A. Short primer on stochastic simulation; Appendix B. Continuous-time HMM filters; Appendix C. Markov processes; Appendix D. Some limit theorems; Bibliography; Index.
This book covers formulation, algorithms, and structural results of partially observed Markov decision processes, linking theory to real-world applications in controlled sensing.