Bayesian Filtering and Smoothing

Simo Särkkä worked, from 2000 to 2010, with Nokia Ltd, Indagon Ltd and Nalco Company in various industrial research projects related to telecommunications, positioning systems and industrial process control. Currently, he is a Senior Researcher with the Department of Biomedical Engineering and Computational Science at Aalto University, Finland, and Adjunct Professor with Tampere University of Technology and Lappeenranta University of Technology. In 2011 he was a visiting scholar with the Signal Processing and Communications Laboratory of the Department of Engineering at the University of Cambridge. His research interests are in state and parameter estimation in stochastic dynamic systems, and in particular, Bayesian methods in signal processing, machine learning, and inverse problems with applications to brain imaging, positioning systems, computer vision and audio signal processing. He is a Senior Member of IEEE.

Preface; Symbols and abbreviations; 1. What are Bayesian filtering and smoothing?; 2. Bayesian inference; 3. Batch and recursive Bayesian estimation; 4. Bayesian filtering equations and exact solutions; 5. Extended and unscented Kalman filtering; 6. General Gaussian filtering; 7. Particle filtering; 8. Bayesian smoothing equations and exact solutions; 9. Extended and unscented smoothing; 10. General Gaussian smoothing; 11. Particle smoothing; 12. Parameter estimation; 13. Epilogue; Appendix: additional material; References; Index.