Juho Kokkala

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profile for Juho Kokkala at Cross Validated, Q&A for people interested in statistics, machine learning, data analysis, data mining, and data visualization

I am currently at Aalto University as a visiting researcher, doing a postdoc period funded by a scholarship from the Post Docs in Companies program. My research is related to Bayesian methods for forecasting elevator traffic.

I defended my Doctor of Science (Tech.) (PhD equivalent) thesis on 4 March, 2016. My thesis advisor was Simo Särkkä who leads the Sensor informatics and medical technology group at the Department of Electrical Engineering and Automation, and my supervisor was Jouko Lampinen. I worked in the Department of Neuroscience and Biomedical Engineering and was also affiliated with the Probabilistic Machine Learning group at the Department of Computer Science. My doctoral research was about Bayesian inference in dynamic systems, especially using particle and sigma-point filtering methods.

Peer-reviewed scientific articles

  1. Juho Kokkala, Arno Solin, and Simo Särkkä (2016). Sigma-Point Filtering and Smoothing Based Parameter Estimation in Nonlinear Dynamic Systems. Accepted for publication in Journal of Advances in Information Fusion. arXiv:1504.06173.
  2. Juho Kokkala and Simo Särkkä (2015). On the (Non-)convergence of Particle Filters with Gaussian Importance Distributions. In Proceedings of the 17th IFAC Symposium on System Identification (SYSID), (IFACPapersOnline, Volume 48, Issue 28), Pages 793-798. Beijing, China, October 2015.
  3. Juho Kokkala and Simo Särkkä (2015). Split-Gaussian Particle Filter. In Proceedings of 23rd European Signal Processing Conference (EUSIPCO), Nice, France, Pages 484-488. August 2015.
  4. 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, December 2015, Pages 84-95. Preprint: arXiv:1409.8502
  5. Juho Kokkala, Arno Solin, and Simo Särkkä (2014). Expectation Maximization Based Parameter Estimation by Sigma-Point and Particle Smoothing. Proceedings of the 17th International Conference on Information Fusion (FUSION). Salamanca, Spain. Preprint as PDF.


  1. Juho Kokkala, Jirka Poropudas, and Kai Virtanen. Rationalizable Strategies in Games With Incomplete Preferences. Submitted manuscript. Preprint in MPRA.

Doctoral thesis