Simo Särkkä mainly focuses on Kalman filter, Mathematical optimization, Applied mathematics, Smoothing and Extended Kalman filter. His studies deal with areas such as Algorithm, Bayesian inference, Filter and Nonlinear system as well as Kalman filter. His Algorithm study incorporates themes from Recursive Bayesian estimation, Particle filter and Hybrid Monte Carlo, Markov chain Monte Carlo.
His specific area of interest is Applied mathematics, where Simo Särkkä studies Stochastic differential equation. The concepts of his Smoothing study are interwoven with issues in State space, Gaussian process and Trajectory. His work deals with themes such as Kriging, Series expansion, Artificial intelligence, Covariance function and Approximation theory, which intersect with Gaussian process.
His main research concerns Applied mathematics, Kalman filter, Algorithm, Gaussian process and Smoothing. Simo Särkkä combines subjects such as Covariance, Gaussian, Mathematical optimization and Nonlinear system with his study of Applied mathematics. His Mathematical optimization research is multidisciplinary, relying on both Particle filter and State space.
His research in Kalman filter is mostly concerned with Extended Kalman filter. His Algorithm study which covers Bayesian probability that intersects with Quadrature. Simo Särkkä focuses mostly in the field of Gaussian process, narrowing it down to matters related to Covariance function and, in some cases, Mathematical analysis.
His scientific interests lie mostly in Applied mathematics, Smoothing, Algorithm, Kalman filter and Gaussian process. His research in Applied mathematics is mostly focused on Stochastic differential equation. Simo Särkkä has researched Smoothing in several fields, including Discrete time and continuous time, Computation, Taylor series and Parallel computing.
His work in Algorithm addresses issues such as Graphics processing unit, which are connected to fields such as Time complexity and Logarithm. His Kalman filter research integrates issues from Filter, Representation, State space, Noise and Nonlinear system. Simo Särkkä works mostly in the field of Gaussian process, limiting it down to topics relating to Kriging and, in certain cases, Computational complexity theory, as a part of the same area of interest.
Simo Särkkä mostly deals with Applied mathematics, Kalman filter, Algorithm, Smoothing and Gaussian process. His Applied mathematics research incorporates elements of Truncation, Orthonormal basis, Simple function, Prior probability and Kernel. Simo Särkkä is interested in Kalman smoother, which is a branch of Kalman filter.
The various areas that Simo Särkkä examines in his Algorithm study include Mixture model, Position and Noise. His Smoothing study combines topics from a wide range of disciplines, such as Stochastic differential equation, Gaussian, Bayesian probability and Regression. His research in Gaussian process intersects with topics in Numerical integration, Probabilistic logic, Quadrature and Extended Kalman filter.
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On Unscented Kalman Filtering for State Estimation of Continuous-Time Nonlinear Systems
S. Sarkka.
IEEE Transactions on Automatic Control (2007)
Recursive Noise Adaptive Kalman Filtering by Variational Bayesian Approximations
S. Sarkka;A. Nummenmaa.
IEEE Transactions on Automatic Control (2009)
Rao-Blackwellized particle filter for multiple target tracking
Simo Särkkä;Aki Vehtari;Jouko Lampinen.
Information Fusion (2007)
Unscented Rauch--Tung--Striebel Smoother
S. Sarkka.
IEEE Transactions on Automatic Control (2008)
Kalman filtering and smoothing solutions to temporal Gaussian process regression models
Jouni Hartikainen;Simo Sarkka.
international workshop on machine learning for signal processing (2010)
Spatiotemporal Learning via Infinite-Dimensional Bayesian Filtering and Smoothing: A Look at Gaussian Process Regression Through Kalman Filtering
S. Sarkka;A. Solin;J. Hartikainen.
IEEE Signal Processing Magazine (2013)
Recursive Bayesian inference on stochastic differential equations
Simo Särkkä.
(2006)
Applied Stochastic Differential Equations
Simo Särkkä;Arno Solin.
(2019)
Recursive outlier-robust filtering and smoothing for nonlinear systems using the multivariate student-t distribution
Robert Piche;Simo Sarkka;Jouni Hartikainen.
international workshop on machine learning for signal processing (2012)
Dynamic retrospective filtering of physiological noise in BOLD fMRI: DRIFTER
Simo Särkkä;Arno Solin;Aapo Nummenmaa;Aapo Nummenmaa;Aki Vehtari.
NeuroImage (2012)
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