Buch, Englisch, 380 Seiten, Format (B × H): 161 mm x 240 mm, Gewicht: 737 g
Buch, Englisch, 380 Seiten, Format (B × H): 161 mm x 240 mm, Gewicht: 737 g
ISBN: 978-1-032-23217-1
Verlag: Chapman and Hall/CRC
Stochastic Differential Equations for Science and Engineering is aimed at students at the M.Sc. and PhD level. The book describes the mathematical construction of stochastic differential equations with a level of detail suitable to the audience, while also discussing applications to estimation, stability analysis, and control. The book includes numerous examples and challenging exercises. Computational aspects are central to the approach taken in the book, so the text is accompanied by a repository on GitHub containing a toolbox in R which implements algorithms described in the book, code that regenerates all figures, and solutions to exercises.
Features:
- Contains numerous exercises, examples, and applications
- Suitable for science and engineering students at Master’s or PhD level
- Thorough treatment of the mathematical theory combined with an accessible treatment of motivating examples
- GitHub repository available at: https://github.com/Uffe-H-Thygesen/SDEbook and https://github.com/Uffe-H-Thygesen/SDEtools
Zielgruppe
Academic and Postgraduate
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik Mathematik Stochastik Bayesianische Inferenz
- Mathematik | Informatik Mathematik Stochastik Stochastische Prozesse
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
- Mathematik | Informatik Mathematik Mathematische Analysis Differentialrechnungen und -gleichungen
- Technische Wissenschaften Technik Allgemein Technik: Allgemeines
Weitere Infos & Material
Introduction. Section I. Fundamentals. 2. Diffusive Transport and Random Walks. 3. Stochastic Experiments and Probability Spaces. 4. Brownian Motion. 5. Linear Dynamic Systems. Section II Stochastic Calculus. 6. Stochastic Integrals. 7. The Stochastic Chain Rule. 8. Existence, Uniqueness, And Numerics. 9. The Kolmogorov Equations. Section III. Applications. 10. State Estimation. 11. Expectations to The Future. 12. Stochastic Stability Theory. 13. Dynamic Optimization. 14. Perspectives.