• Publications
  • Influence
Bayesian Filtering and Smoothing
  • S. Särkkä
  • Computer Science
    Institute of Mathematical Statistics textbooks
  • 2013
TLDR
This compact, informal introduction for graduate students and advanced undergraduates presents the current state-of-the-art filtering and smoothing methods in a unified Bayesian framework and learns what non-linear Kalman filters and particle filters are, how they are related, and their relative advantages and disadvantages.
Recursive Noise Adaptive Kalman Filtering by Variational Bayesian Approximations
This article considers the application of variational Bayesian methods to joint recursive estimation of the dynamic state and the time-varying measurement noise parameters in linear state space
On Unscented Kalman Filtering for State Estimation of Continuous-Time Nonlinear Systems
  • S. Särkkä
  • Mathematics
    IEEE Trans. Autom. Control.
  • 17 September 2007
This paper considers the application of the unscented Kalman filter (UKF) to continuous-time filtering problems, where both the state and measurement processes are modeled as stochastic differential
Optimal Filtering with Kalman Filters and Smoothers
In this paper we present a documentation for an optimal filtering toolbox for the mathematical software package Matlab. The toolbox features many filtering methods for discrete-time state space
Applied Stochastic Differential Equations
TLDR
The topic of this book is stochastic differential equations (SDEs), which are differential equations that produce a different “answer” or solution trajectory each time they are solved, and the emphasis is on applied rather than theoretical aspects of SDEs.
Recursive Bayesian inference on stochastic differential equations
TLDR
The main contributions of this thesis are to show how the recently developed discrete-time unscented Kalman filter, particle filter, and the corresponding smoothers can be applied in the continuous-discrete setting.
Unscented Rauch-Tung-Striebel Smoother
  • S. Särkkä
  • Mathematics
    IEEE Trans. Autom. Control.
  • 8 April 2008
TLDR
A new Rauch-Tung-Striebel type form of the fixed-interval unscented Kalman smoother is derived, which is not based on running two independent filters forward and backward in time, but on a separate backward smoothing pass which recursively computes corrections to the forward filtering result.
Hilbert space methods for reduced-rank Gaussian process regression
TLDR
The method is compared to previously proposed methods theoretically and through empirical tests with simulated and real data, and shows that the approximation becomes exact when the size of the compact subset and the number of eigenfunctions go to infinity.
Physics-Informed Machine Learning
TLDR
Some of the prevailing trends in embedding physics into machine learning are reviewed, some of the current capabilities and limitations are presented and diverse applications of physicsinformed learning both for forward and inverse problems, including discovering hidden physics and tackling highdimensional problems are discussed.
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