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With this hands-on introduction readers will learn what SDEs are all about and how they should use them in practice.
List of contents
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.
About the author
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).Arno Solin is an Academy of Finland Postdoctoral Researcher with Aalto University, Finland and Technical Advisor at IndoorAtlas Ltd. His research interests focus on models and applications in sensor fusion for tracking and navigation, brain imaging, and machine learning problems. He has published over twenty peer-reviewed scientific papers, and has won several hackathons and competitions in mathematical modeling, including the 2014 Schizophrenia classification on Kaggle.
