Simo Särkkä

Associate Professor in Sensor informatics and medical technology at Department of Electrical Engineering and Automation (EEA), Aalto University
Academy Research Fellow, Aalto University
Docent, Adjunct Professor (both at TUT and LUT)
Associate Editor of IEEE Signal Processing Letters

Postal Address:

P.O.Box 12200
FIN-00076 AALTO
FINLAND

Street Address:

Room F305, F-Talo, 3rd Floor
Rakentajanaukio 2c
Espoo, Finland

Contact:

Mobile: +358 50 512 4393
Email: simo.sarkka@aalto.fi
Skype: simosarkka
Web: users.aalto.fi/~ssarkka
Twitter: @simosarkka

Current doctoral (PhD) students / visitors

Arno Solin (IndoorAtlas Ltd.)
Juha Sarmavuori (Nokia Ltd.)
Toni Karvonen (Aalto University)
Filip Tronarp (Aalto University)
Jakub Prüher (visiting from University of West Bohemia)

Former doctoral students (Drs. now)

Juho Kokkala (PoDoCo Postdoc)
Isambi S. Mbalawata (University of Dar Es Salaam, Tanzania)
Jouni Hartikainen (Rocsole Ltd.)

Biography

Current/previous positions:

  • 2015- Associate Professor, EEA, Aalto University
  • 2015- Director of IndoorAtlas Ltd.
  • 2013- Academy Research Fellow, NBE/BECS, Aalto University
  • 2013- Technical advisor of IndoorAtlas Ltd.
  • 2012- Docent (Adj.Prof.), Lappeenranta University of Technology
  • 2011- Docent (Adj.Prof.), Tampere University of Technology
  • 2007- Independent consultant
  • 2014 (May) Visiting Scholar, Chalmers University of Technology
  • 2013 (Oct-Dec) Visiting Professor, University of Oxford
  • 2011 (Apr-Dec) Visiting Scholar, University of Cambridge
  • 2010-2013 Senior Researcher, Aalto University
  • 2007-2009 Staff Scientist, Nalco / Nalco-Mobotec
  • 2002-2007 R&D Manager, Indagon Ltd.
  • 2000-2002 Research Engineer, Nokia Ltd.

Simo Särkkä received his Master of Science (Tech.) degree (with distinction) in engineering physics and mathematics, and Doctor of Science (Tech.) degree (with distinction) in electrical and communications engineering from Helsinki University of Technology, Espoo, Finland, in 2000 and 2006, respectively. From 2000 to 2010 he worked with Nokia Ltd., Indagon Ltd., and Nalco Company in various industrial research projects related to telecommunications, positioning systems, and industrial process control. From 2010 to 2013 he worked as a Senior Researcher with the Department of Biomedical Engineering and Computational Science (BECS) at Aalto University, Finland.

Currently, Dr. Särkkä is an Associate Professor and Academy Research Fellow with Aalto University, Technical Advisor and Director of IndoorAtlas Ltd., and an Adjunct Professor with Tampere University of Technology and Lappeenranta University of Technology. In 2013 he was a Visiting Professor with the Department of Statistics of Oxford University and in 2011 he was a Visiting Scholar with the Department of Engineering at the University of Cambridge, UK. His research interests are in multi-sensor data processing systems with applications in location sensing, health technology, machine learning, inverse problems, and brain imaging. He has authored or coauthored ~60 peer-reviewed scientific articles and has 3 granted patents. His first book "Bayesian Filtering and Smoothing" was recently published via the Cambridge University Press. He is a Senior Member of IEEE and serving as an Associate Editor of IEEE Signal Processing Letters from August 2015.


Research Activities

  • Applications

    • Embedded systems in health technology
    • Signal processing and state estimation in brain imaging (fMRI/MEG/EEG/DOT)
    • Spatio-temporal modeling in machine learning, inverse problems, and Kriging.
    • Location sensing, target tracking, audio signal processing.
    • Applications in medicine, biology, RF/RFID systems, telematics, optical/video tracking, inertial navigation, robotics, audio systems, etc.
  • Bayesian Inference Methods for Stochastic Dynamic Systems

    • Non-Linear Kalman/Bayesian filtering and smoothing
    • Continuous-time stochastic models and stochastic differential equations (SDEs)
    • Particle filtering and sequential Monte Carlo methods
  • Bayesian Inference Methods for Spatial and Spatio-Temporal Systems

    • State-space, sparse, and reduced rank methods in Gaussian process regression.
    • Stochastic partial/pseudo differential equations (SPDE).
    • Infinite-dimensional/distributed-parameter Kalman filtering and smoothing.
  • Theoretical Analysis and Other Methodology

    • Convergence and stability analysis of approximate Bayesian filters and smoothers
    • Theoretical analysis of Gaussian process regressors
    • Advanced Markov chain Monte Carlo methods

Publications

My Google Scholar profile: http://scholar.google.com/citations?user=QVhmc9cAAAAJ

See also Aalto People page

The PDF preprints below are draft versions of the articles and they are here to give people an opportunity to check the relevance of the articles before purchasing the final articles from the publisher. Please send me an email if you want the latest preprints of the submitted articles.

Books

  1. Simo Särkkä (2013). Bayesian Filtering and Smoothing. Cambridge University Press. Available from Cambridge University Press at http://www.cambridge.org/sarkka, from this CUP-link, or from, e.g., Amazon UK or Amazon USA.

    Although the more convenient (and quite affordable) printed version of the book can be purchased from the above sources, with permission from the publisher, I am providing a PDF version of the book here:

    This PDF version is made available for personal use. The copyright in all material rests with the author (Simo Särkkä). Commercial reproduction is prohibited, except as authorised by the author and publisher.

  2. 希莫•萨日伽 (2015). 贝叶斯滤波与平滑. 国防工业出版社. Chinese translation of "Bayesian filtering and smoothing". Available, e.g., from Amazon China.

Journals

  1. Ángel F. García-Fernández, Lennart Svensson, Simo Särkkä. Iterated posterior linearisation smoother. Accepted for publication in IEEE Transactions on Automatic Control.
  2. Patrick R. Conrad, Mark Girolami, Simo Särkkä, Andrew Stuart, Konstantinos Zygalakis. Probability Measures for Numerical Solutions of Differential Equations. Accepted for publication in Statistics and Computing. (arXiv)
  3. Juho Kokkala, Arno Solin, Simo Särkkä (2016). Sigma-Point Filtering and Smoothing Based Parameter Estimation in Nonlinear Dynamic Systems. Journal of Advances in Information Fusion, 11(1), 15-30. (arXiv)
  4. Simo Särkkä, Jouni Hartikainen, Lennart Svensson, Fredrik Sandblom (2016). On the relation between Gaussian process quadratures and sigma-point methods. Journal of Advances in Information Fusion, 11(1), 31-46. (arXiv)
  5. Fredrik Lindsten, Pete Bunch, Simo Särkkä, Thomas Schön, Simon Godsill (2016). Rao-Blackwellized particle smoothers for conditionally linear Gaussian models. IEEE Journal of Selected Topics in Signal Processing, Volume 10, Number 2, Pages 353-365. (arXiv)
  6. I. S. Mbalawata and S. Särkkä. (2016) Moment Conditions for Convergence of Particle Filters with Unbounded Importance Weights. Signal Processing, Volume 116, Pages 133-138. (arXiv)
  7. Ángel F. García-Fernández, Lennart Svensson, Mark R. Morelande, Simo Särkkä (2015). Posterior linearisation filter: principles and implementation using sigma points. IEEE Transactions on Signal Processing, Volume 63, Number 20, Pages 5561-5573 (DOI, Preprint)
  8. Xi Chen, Simo Särkkä, Simon Godsill (2015). A Bayesian Particle Filtering Method For Brain Source Localisation. Digital Signal Processing, Volume 47, Pages 192-204. (arXiv)
  9. Sean Anderson, Timothy D. Barfoot, Chi Hay Tong, and Simo Särkkä (2015). Batch Nonlinear Continuous-Time Trajectory Estimation as Exactly Sparse Gaussian Process Regression. Autonomous Robots, Volume 39, Issue 3, Page 221-238. (axXiv)
  10. Juho Kokkala and Simo Särkkä (2015). Combining Particle MCMC with Rao-Blackwellized Monte Carlo Data Association for Parameter Estimation in Multiple Target Tracking. Digital Signal Processing, Volume 47, Pages 84-95, (arXiv)
  11. J. Ala-Luhtala, S. Särkkä, and R. Piché (2015). Gaussian filtering and variational approximations for Bayesian smoothing in continuous-discrete stochastic dynamic systems. Signal Processing, Volume 111, Pages 124-136. (DOI, arXiv)
  12. S. Särkkä, J. Hartikainen, I. S. Mbalawata, H. Haario (2015). Posterior Inference on Parameters of Stochastic Differential Equations via Non-Linear Gaussian Filtering and Adaptive MCMC. Statistics and Computing, Volume 25, Issue 2, Pages 427-437. (DOI, Preprint)
  13. I. S. Mbalawata, S. Särkkä, M. Vihola, H. Haario (2015). Adaptive Metropolis Algorithm Using Variational Bayesian Adaptive Kalman Filter. In Computational Statistics and Data Analysis, Volume 83, Pages 101-115. (arXiv, DOI)
  14. S.M.J. Lyons, S. Särkkä, and A.J. Storkey (2014). Series Expansion Approximations of Brownian Motion for Non-Linear Kalman Filtering of Diffusion Processes. IEEE Transactions on Signal Processing, Volume 62, Issue 6, Pages 1514-1524. (DOI, arXiv)
  15. A. Solin and S. Särkkä (2013). Infinite-Dimensional Bayesian Filtering for Detection of Quasi-Periodic Phenomena in Spatio-Temporal Data. Physical Review E, Volume 88, Issue 5, 052909. (arXiv, DOI)
  16. S. Särkkä, A. Solin, and J. Hartikainen (2013). Spatio-Temporal Learning via Infinite-Dimensional Bayesian Filtering and Smoothing. IEEE Signal Processing Magazine, Volume 30, Issue 4, Pages 51-61. (Preprint, DOI)
  17. S. Särkkä and J. Sarmavuori (2013). Gaussian Filtering and Smoothing for Continuous-Discrete Dynamic Systems. Signal Processing, Volume 93. Issue 2, Pages 500-510. (Preprint, DOI, Matlab toolbox)
  18. I. S. Mbalawata, S. Särkkä, and H. Haario (2013). Parameter Estimation in Stochastic Differential Equations with Markov Chain Monte Carlo and Non-Linear Kalman Filtering. Computational Statistics, Volume 28, Issue 3, Pages 1195-1223 (DOI)
  19. S. Särkkä, A. Solin, A. Nummenmaa, A. Vehtari, T. Auranen, S. Vanni, F.-H. Lin (2012). Dynamic Retrospective Filtering of Physiological Noise in BOLD fMRI: DRIFTER. NeuroImage, Volume 60, Issue 2, Pages 1517-1527. (DOI, Preprint, Matlab toolbox)
  20. J. Sarmavuori and S. Särkkä (2012). Fourier-Hermite Kalman Filter. IEEE Transactions on Automatic Control, Volume 57, Issue 6, Pages 1511-1515. (DOI, Preprint)
  21. S. Särkkä, V. Viikari, M. Huusko, and K. Jaakkola (2012). Phase-Based UHF RFID Tracking with Non-Linear Kalman Filtering and Smoothing. IEEE Sensors Journal, Volume 12, Issue 5, Pages 904-910. (DOI, Preprint)
  22. S. Särkkä and A. Huovilainen (2011). Accurate Discretization of Analog Audio Filters with Application to Parametric Equalizer Design. IEEE Transactions on Audio, Speech, and Language Processing, Volume 19, Issue 8, Pages 2486-2493. (DOI , Preprint, Matlab code, C++ code, VST Effect for OS X)
  23. P. Hiltunen, S. Särkkä, I. Nissilä, A. Lajunen and J. Lampinen (2011). State space regularization in the nonstationary inverse problem for diffuse optical tomography. Inverse Problems, Volume 27, Number 2. (DOI)
  24. S. Särkkä and J. Hartikainen (2010). On Gaussian Optimal Smoothing of Non-Linear State Space Models. IEEE Transactions on Automatic Control, Volume 55, Issue 8, Pages 1938-1941. (DOI, Preprint, Matlab toolbox). See also errata DOI or Preprint.
  25. S. Särkkä (2010). Continuous-Time and Continuous-Discrete-Time Unscented Rauch-Tung-Striebel Smoothers. Signal Processing, Volume 90, Issue 1, Pages 225-235. (DOI, Preprint)
  26. S. Särkkä and A. Nummenmaa (2009). Recursive Noise Adaptive Kalman Filtering by Variational Bayesian Approximations. IEEE Transactions on Automatic Control, Volume 54, Issue 3, Pages 596-600. (DOI, Preprint, Matlab code)
  27. S. Särkkä and T. Sottinen (2008). Application of Girsanov Theorem to Particle Filtering of Discretely Observed Continuous-Time Non-Linear Systems. Bayesian Analysis, Volume 3, Number 03, Pages 555-584. (DOI)
  28. S. Särkkä (2008). Unscented Rauch-Tung-Striebel Smoother. IEEE Transactions on Automatic Control, Volume 53, Issue 3, Pages 845-849. (DOI, Preprint, Matlab toolbox)
  29. S. Särkkä, A. Vehtari, and J. Lampinen (2007). Rao-Blackwellized Particle Filter for Multiple Target Tracking. Information Fusion Journal, Volume 8, Issue 1, Pages 2-15. (DOI, Preprint, Matlab toolbox)
  30. S. Särkkä, A. Vehtari, and J. Lampinen (2007). CATS Benchmark Time Series Prediction by Kalman Smoother with Cross-Validated Noise Density. Neurocomputing, Volume 70, Issues 13-15, Pages 2331-2341. (DOI Preprint)
  31. S. Särkkä (2007). On Unscented Kalman Filtering for State Estimation of Continuous-Time Nonlinear Systems. IEEE Transactions on Automatic Control, Volume 52, Issue 9, Pages 1631-1641. (DOI, Preprint)

Conference Proceedings

  1. Toni Karvonen and Simo Särkkä (2016). Approximate state-space Gaussian processes via spectral transformation. To appear in Proceedings of IEEE International Workshop on Machine Learning for Signal Processing (MLSP).
  2. Jakub Prüher and Simo Särkkä (2016). On The Use Of Gradient Information In Gaussian Process Quadratures. To appear in Proceedings of IEEE International Workshop on Machine Learning for Signal Processing (MLSP).
  3. Toni Karvonen and Simo Särkkä (2016). Fourier-Hermite Series for Stochastic Stability Analysis of Non-Linear Kalman Filters. To appear in Proceedings of FUSION 2016.
  4. Filip Tronarp, Roland Hostettler, and Simo Särkkä (2016). Sigma-Point Filtering for Nonlinear Systems with Non-Additive Heavy-Tailed Noise. To appear in Proceedings of FUSION 2016.
  5. Arno Solin, Simo Särkkä, Juho Kannala, and Esa Rahtu (2016). Terrain Navigation in the Magnetic Landscape: Particle Filtering for Indoor Positioning. To appear in Proceedings of ENC 2016. (PDF)
  6. Andreas Svensson, Arno Solin, Simo Särkkä, Thomas Schön (2016). Computationally Efficient Bayesian Learning of Gaussian Process State Space Models. In Proceedings of AISTATS, Pages 213-221. (arXiv)
  7. Simo Särkkä, Eric Moulines (2016). On The L_p-Convergence of a Girsanov Theorem Based Particle Filter. In Proceedings of ICASSP. (PDF)
  8. Andreas Svensson, Thomas B. Schön, Arno Solin, Simo Särkkä (2015). Nonlinear State Space Model Identification Using a Regularized Basis Function Expansion. In Proceedings of the 6th IEEE international workshop on computational advances in multi-sensor adaptive processing (CAMSAP). (arXiv)
  9. Juho Kokkala and Simo Särkkä (2015). On the (Non-)convergence of Particle Filters with Gaussian Importance Distributions. In Proceedings of SYSID 2015. (DOI)
  10. Simo Särkkä, Ville Tolvanen, Juho Kannala, and Esa Rahtu (2015). Adaptive Kalman Filtering and Smoothing for Gravitation Tracking in Mobile Systems. In Proceedings of International Conference on Indoor Positioning and Indoor Navigation (IPIN) 2015. (PDF)
  11. Juho Kokkala and Simo Särkkä (2015). Split-Gaussian Particle Filter. In Proceedings of EUSIPCO 2015. (PDF)
  12. Arno Solin and Simo Särkkä (2015). State Space Methods for Efficient Inference in Student-t Process Regression. In Proceedings of AISTATS. (PDF)
  13. Jayaprasad Bojja, Jussi Collin, Simo Särkkä, and Jarmo Takala (2015). Pedestrian Localization in Moving Platforms Using Dead Reckoning, Particle Filtering and Map Matching. In Proceedings of ICASSP.
  14. A. Solin and S. Särkkä (2014). The 10th Annual MLSP Competition: First Place. In Proceedings of MLSP. (Preprint as PDF)
  15. A. Solin and S. Särkkä (2014). Gaussian quadratures for state space approximation of scale mixtures of squared exponential covariance functions. In Proceedings of MLSP. (Preprint as PDF)
  16. S. Särkkä and R. Piché (2014). On convergence and accuracy of state-space approximations of squared exponential covariance functions. In Proceedings of MLSP. (Preprint as PDF, Code in Bitbucket)
  17. I. S. Mbalawata and S. Särkkä (2014). Weight Moment Conditions for L4 Convergence of Particle Filters for Unbounded Test Functions. In Proceedings of EUSIPCO 2014. (Preprint as PDF)
  18. S. Särkkä, V. Viikari, K. Jaakkola (2014). RFID-Based Butterfly Location Sensing System. In Proceedings of EUSIPCO 2014. (Preprint as PDF)
  19. J. Kokkala, A. Solin, and S. Särkkä (2014). Expectation Maximization Based Parameter Estimation by Sigma-Point and Particle Smoothing. In Proceedings of FUSION 2014. (Preprint as PDF)
  20. S. Särkkä, J. Hartikainen, L. Svensson, and F. Sandblom (2014). Gaussian Process Quadratures in Nonlinear Sigma-Point Filtering and Smoothing. In Proceedings of FUSION 2014. (Preprint as PDF)
  21. T. D. Barfoot, C. H. Tong, and S. Särkkä (2014). Batch Continuous-Time Trajectory Estimation as Exactly Sparse Gaussian Process Regression. In Proceedings of Robotics: Science and Systems (RSS). (PDF)
  22. I. S. Mbalawata and S. Särkkä (2014). On The L4 Convergence of Particle Filters with General Importance Distributions. In Proceedings of ICASSP. (Preprint as PDF)
  23. A. Solin and S. Särkkä (2014). Explicit Link Between Periodic Covariance Functions and State Space Models. JMLR Workshop and Conference Proceedings Volume 33 (AISTATS 2014), Pages 904-912. (Preprint as PDF, PDF)
  24. A. Solin, S. Särkkä, A. Nummenmaa, A. Vehtari, T. Auranen, F.-H. Lin (2014). Catching Physiological Noise: Comparison of DRIFTER in Image and k-Space. In Proceedings of ISMRM 2014 (abstract and poster).
  25. X. Chen, S. Särkkä, and S. Godsill (2013). Probabilistic Initiation and Termination for MEG Multiple Dipole Localization Using Sequential Monte Carlo Methods. In Proceedings of FUSION 2013.
  26. S. Särkkä and A. Solin (2013). Continuous-Space Gaussian Process Regression and Generalized Wiener Filtering with Application to Learning Curves. In Proceedings of SCIA 2013. (Preprint as PDF, DOI)
  27. S. Särkkä and J. Hartikainen (2013). Non-Linear Noise Adaptive Kalman Filtering via Variational Bayes. In Proceedings of MLSP 2013. (Preprint as PDE)
  28. A. Solin, E. Glerean, and S. Särkkä (2013). Time-Frequency Dynamics of Brain Connectivity by Stochastic Oscillator Models and Kalman Filtering. In Proceedings of OHBM 2013 (abstract and poster).
  29. A. Solin, S. Särkkä, A. Nummenmaa, A. Vehtari, T. Auranen, S. Vanni, F.-H. Lin (2013). Volumetric Space-Time Structure of Physiological Noise in BOLD fMRI. In Proceedings of ISMRM 2013 (abstract and poster). (Abstract, Poster)
  30. S.M.J. Lyons, A.J. Storkey, and S. Särkkä (2012). The Coloured Noise Expansion and Parameter Estimation of Diffusion Processes. Proceedings of NIPS, pages 1961-1969. (PDF)
  31. R. Piché, S. Särkkä and J. Hartikainen (2012). Recursive Outlier-Robust Filtering and Smoothing for Nonlinear Systems Using the Multivariate Student-t Distribution. In Proceedings of MLSP. (Preprint as PDF)
  32. J. Sarmavuori and S. Särkkä (2012). Fourier-Hermite Rauch-Tung-Striebel Smoother. Proceedings of EUSIPCO, pages 2109-2113. (Preprint as PDF)
  33. S. Särkkä, P. Bunch and S. J. Godsill (2012). A Backward-Simulation Based Rao-Blackwellized Particle Smoother for Conditionally Linear Gaussian Models. Proceedings of SYSID 2012, pages 506-511. (invited paper). (Preprint as PDF)
  34. S. Särkkä and A. Solin (2012). On Continuous-Discrete Cubature Kalman Filtering. Proceedings of SYSID 2012, pages 1210-1215. (Preprint)
  35. J. Hartikainen, M. Seppänen and S. Särkkä (2012). State-Space Inference for Non-Linear Latent Force Models with Application to Satellite Orbit Prediction. Proceedings of The 29th International Conference on Machine Learning (ICML 2012). (PDF)
  36. S. Särkkä and J. Hartikainen (2012). Infinite-Dimensional Kalman Filtering Approach to Spatio-Temporal Gaussian Process Regression. JMLR Workshop and Conference Proceedings Volume 22: AISTATS 2012, Pages 993-1001. (Preprint)
  37. S. Särkkä, A. Solin, A. Nummenmaa, A. Vehtari, T. Auranen, S. Vanni and F.-H. Lin (2012). Identification of Spatio-Temporal Oscillatory Signal Structure in Cerebral Hemodynamics Using DRIFTER. Proceedings of ISMRM 2012. (E-Poster, Abstract)
  38. J. Hartikainen and S. Särkkä (2011). Sequential Inference for Latent Force Models. Proceedings of The 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011) (Preprint)
  39. J. Hartikainen, J. Riihimäki and S. Särkkä (2011). Sparse Spatio-Temporal Gaussian Processes with General Likelihoods. Proceedings of International Conference on Artificial Neural Networks (ICANN) (DOI, Preprint)
  40. S. Särkkä (2011). Linear Operators and Stochastic Partial Differential Equations in Gaussian Process Regression. Proceedings of International Conference on Artificial Neural Networks (ICANN) (DOI, Preprint)
  41. S. Särkkä (2011). Learning Curves for Gaussian Processes via Numerical Cubature Integration. Proceedings of International Conference on Artificial Neural Networks (ICANN) (DOI, Preprint)
  42. S. Särkkä, A. Nummenmaa, A. Solin, A. Vehtari, T. Witzel, T. Auranen, S. Vanni, M.S. Hämäläinen, and F-H. Lin. Dynamical statistical modeling of physiological noise for fast BOLD fMRI. Proceedings of ISMRM 2011. (E-Poster)
  43. J. Hartikainen and S. Särkkä (2010). Kalman Filtering and Smoothing Solutions to Temporal Gaussian Process Regression Models. Proceedings of IEEE International Workshop on Machine Learning for Signal Processing (MLSP) (Preprint)
  44. S. Särkkä and J. Hartikainen (2010). Sigma Point Methods in Optimal Smoothing of Non-Linear Stochastic State Space Models. Proceedings of IEEE International Workshop on Machine Learning for Signal Processing (MLSP) (Preprint)
  45. S. Särkkä, A. Vehtari, and J. Lampinen (2007). Prediction of ESTSP Competition Time Series by Unscented Kalman Filter and RTS Smoother. Proceedings of ESTSP 2007 (Preprint)
  46. S. Särkkä (2006). On Sequential Monte Carlo Sampling of Discretely Observed Stochastic Differential Equations. Proceedings of NSSPW, (Preprint)
  47. S. Särkkä, A. Vehtari, and J. Lampinen (2004). Time series prediction by Kalman smoother with cross validated noise density. Proceedings of IJCNN 2004. The Winner of Time Series Prediction Competition - The CATS Benchmark (Preprint)
  48. S. Särkkä, A. Vehtari, and J. Lampinen (2004). Rao-Blackwellized Monte Carlo data association for multiple target tracking. Proceedings of FUSION 2004 (Preprint, Matlab toolbox)
  49. A. Vehtari, S. Särkkä, and J. Lampinen (2000). On MCMC sampling in Bayesian MLP neural networks. Proceedings of the IJCNN 2000 (Preprint as ps.gz)

Doctoral Dissertation

  1. S. Särkkä (2006). Recursive Bayesian Inference on Stochastic Differential Equations. Doctoral dissertation, Helsinki University of Technology (Thesis as PDF)

Technical Reports

  1. S. Särkkä and J. Hartikainen. Variational Bayesian Adaptation of Noise Covariances in Non-Linear Kalman Filtering. (arXiv)
  2. S. Särkkä (2007). Notes on Quaternions. Technical report. (Report as PDF)
  3. S. Särkkä, Toni Tamminen, Aki Vehtari, and Jouko Lampinen (2004). Probabilistic methods in multiple target tracking - Review and bibliography. Technical report B36, ISBN 951-22-6938-4, Helsinki University of Technology. Laboratory of Computational Engineering (Report as PDF)
  4. S. Särkkä (2000). Bayesilaiset menetelmät audiovisuaalisen puheen havaitsemisen mallintamisessa. Diploma thesis, (in Finnish) (Thesis as ps.gz)
  5. S. Särkkä (1999). MCMC-menetelmät ja diagnostiikat. Technical report (in Finnish). (HTML, ps.gz)

Course material:

  1. Simo Särkkä and Arno Solin (2014). Applied Stochastic Differential Equations. Lecture notes of the course Becs-114.4202 Special Course in Computational Engineering II held in Autumn 2014. (Booklet as PDF, Slides and exercises). (2012 material is here).
  2. S. Särkkä (2012). Bayesian Estimation of Time-Varying Systems: Discrete-Time Systems. Lectures notes of the course S-114.4610 held in Spring 2012 (Booklet as PDF, Slides and Exercises). (2011 material is here).

Patents

  1. WO/2004/111677
  2. WO/2008/034944
  3. WO/2006/108921

Working papers

  1. Arno Solin, Pasi Jylänki, Jaakko Kauramäki, Tom Heskes, Marcel A. J. van Gerven, Simo Särkkä. Regularizing Solutions to the MEG Inverse Problem Using Space-Time Separable Covariance Functions. Submitted. (arXiv)
  2. Arno Solin, Manon Kok, Niklas Wahlström, Thomas B. Schön, Simo Särkkä. Modeling and interpolation of the ambient magnetic field by Gaussian processes. Submitted. (arXiv)
  3. Lassi Roininen, Sari Lasanen, Mikko Orispää,and Simo Särkkä. Sparse Approximations of Fractional Matern Fields. Submitted. (arXiv)
  4. A. Solin and S. Särkkä. Hilbert Space Methods for Reduced-Rank Gaussian Process Regression. Submitted. (arXiv)

Software

Software Packages

Some Matlab toolboxes where I have contributed to (see also the code examples linked in the publication list above):


Teaching

Some courses etc. that I am giving / have given / will give soon: