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Englisch
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Beschreibung
With this hands-on introduction readers will learn what SDEs are all about and how they should use them in practice.
With this hands-on introduction readers will learn what SDEs are all about and how they should use them in practice.
Über den Autor
Simo Särkkä is Associate Professor of Electrical Engineering and Automation at Aalto University, Finland, Technical Advisor at IndoorAtlas Ltd., and Adjunct Professor at Tampere University of Technology and Lappeenranta University of Technology. His research interests are in probabilistic modeling and sensor fusion for location sensing, health technology, and machine learning. He has authored over ninety peer-reviewed scientific articles as well as one book, titled Bayesian Filtering and Smoothing (Cambridge, 2013).
Inhaltsverzeichnis
1. Introduction; 2. Some background on ordinary differential equations; 3. Pragmatic introduction to stochastic differential equations; 4. Ito calculus and stochastic differential equations; 5. Probability distributions and statistics of SDEs; 6. Statistics of linear stochastic differential equations; 7. Useful theorems and formulas for SDEs; 8. Numerical simulation of SDEs; 9. Approximation of nonlinear SDEs; 10. Filtering and smoothing theory; 11. Parameter estimation in SDE models; 12. Stochastic differential equations in machine learning; 13. Epilogue.
Details
Erscheinungsjahr: | 2019 |
---|---|
Fachbereich: | Wahrscheinlichkeitstheorie |
Genre: | Mathematik |
Rubrik: | Naturwissenschaften & Technik |
Medium: | Buch |
ISBN-13: | 9781316510087 |
ISBN-10: | 1316510085 |
Sprache: | Englisch |
Ausstattung / Beilage: | HC gerader Rücken kaschiert |
Einband: | Gebunden |
Autor: | Särkkä, Simo |
Hersteller: | Cambridge University Press |
Maße: | 235 x 157 x 22 mm |
Von/Mit: | Simo Särkkä |
Erscheinungsdatum: | 02.05.2019 |
Gewicht: | 0,629 kg |
Über den Autor
Simo Särkkä is Associate Professor of Electrical Engineering and Automation at Aalto University, Finland, Technical Advisor at IndoorAtlas Ltd., and Adjunct Professor at Tampere University of Technology and Lappeenranta University of Technology. His research interests are in probabilistic modeling and sensor fusion for location sensing, health technology, and machine learning. He has authored over ninety peer-reviewed scientific articles as well as one book, titled Bayesian Filtering and Smoothing (Cambridge, 2013).
Inhaltsverzeichnis
1. Introduction; 2. Some background on ordinary differential equations; 3. Pragmatic introduction to stochastic differential equations; 4. Ito calculus and stochastic differential equations; 5. Probability distributions and statistics of SDEs; 6. Statistics of linear stochastic differential equations; 7. Useful theorems and formulas for SDEs; 8. Numerical simulation of SDEs; 9. Approximation of nonlinear SDEs; 10. Filtering and smoothing theory; 11. Parameter estimation in SDE models; 12. Stochastic differential equations in machine learning; 13. Epilogue.
Details
Erscheinungsjahr: | 2019 |
---|---|
Fachbereich: | Wahrscheinlichkeitstheorie |
Genre: | Mathematik |
Rubrik: | Naturwissenschaften & Technik |
Medium: | Buch |
ISBN-13: | 9781316510087 |
ISBN-10: | 1316510085 |
Sprache: | Englisch |
Ausstattung / Beilage: | HC gerader Rücken kaschiert |
Einband: | Gebunden |
Autor: | Särkkä, Simo |
Hersteller: | Cambridge University Press |
Maße: | 235 x 157 x 22 mm |
Von/Mit: | Simo Särkkä |
Erscheinungsdatum: | 02.05.2019 |
Gewicht: | 0,629 kg |
Warnhinweis