His scientific interests lie mostly in Particle filter, Artificial intelligence, Nonlinear system, Inertial measurement unit and Mathematical optimization. His Particle filter research includes elements of Kalman filter, Algorithm and Monte Carlo method, Markov chain Monte Carlo. The concepts of his Artificial intelligence study are interwoven with issues in Machine learning and Computer vision.
His studies in Nonlinear system integrate themes in fields like Dynamical systems theory, Estimation, State space and System identification. His System identification study integrates concerns from other disciplines, such as Smoothing, Maximum likelihood and Gaussian process. His study in Inertial measurement unit is interdisciplinary in nature, drawing from both Gyroscope and Sensor fusion.
Thomas B. Schön mostly deals with Artificial intelligence, Particle filter, Algorithm, Mathematical optimization and Nonlinear system. Thomas B. Schön has included themes like Machine learning and Computer vision in his Artificial intelligence study. He works mostly in the field of Particle filter, limiting it down to topics relating to Markov chain Monte Carlo and, in certain cases, Markov chain.
His Algorithm study combines topics in areas such as Inference, Gaussian process, Bayesian inference, Sampling and Probabilistic logic. His Mathematical optimization research incorporates themes from Identification, Robustness and Expectation–maximization algorithm. His Nonlinear system study integrates concerns from other disciplines, such as Nonlinear system identification, System identification, State space, Maximum likelihood and Applied mathematics.
His primary scientific interests are in Artificial intelligence, Machine learning, Nonlinear system, Mycobacterium tuberculosis and Artificial neural network. His work deals with themes such as Pattern recognition, Computer vision and Code, which intersect with Artificial intelligence. His research on Machine learning also deals with topics like
Thomas B. Schön conducts interdisciplinary study in the fields of Particle filter and Bias of an estimator through his research. His Nonlinear system research incorporates elements of Nonlinear system identification, Estimation theory, Maximum likelihood, Mathematical optimization and Applied mathematics. He has researched Maximum likelihood in several fields, including State space and Gaussian noise.
His primary areas of investigation include Artificial intelligence, Artificial neural network, Deep learning, Machine learning and Mycobacterium tuberculosis complex. The concepts of his Artificial intelligence study are interwoven with issues in Computer vision and Code. His studies deal with areas such as Uncertainty quantification, Probability distribution, Bayesian probability and Python as well as Computer vision.
His Artificial neural network research integrates issues from Function, 12 lead ecg and Linear map. Thomas B. Schön combines subjects such as Identification, Representation, Nonlinear system, Range and Flexibility with his study of Deep learning. His research on Machine learning also deals with topics like
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Marginalized particle filters for mixed linear/nonlinear state-space models
T. Schon;F. Gustafsson;P.-J. Nordlund.
IEEE Transactions on Signal Processing (2005)
System identification of nonlinear state-space models
Thomas B. Schön;Adrian Wills;Brett Ninness.
Marginalized Particle Filters for Nonlinear State-space Models
Thomas Schön;Fredrik Gustafsson;Per-Johan Nordlund.
On Resampling Algorithms for Particle Filters
Jeroen D. Hol;Thomas B. Schon;Fredrik Gustafsson.
2006 IEEE Nonlinear Statistical Signal Processing Workshop (2006)
Using Inertial Sensors for Position and Orientation Estimation
Manon Kok;Jeroen D. Hol;Thomas B. Schön.
Identification of Hammerstein-Wiener models
Adrian Wills;Thomas B. SchöN;Lennart Ljung;Brett Ninness.
Particle gibbs with ancestor sampling
Fredrik Lindsten;Michael I. Jordan;Thomas B. Schön.
Journal of Machine Learning Research (2014)
Automatic diagnosis of the 12-lead ECG using a deep neural network
Antônio H. Ribeiro;Antônio H. Ribeiro;Manoel Horta Ribeiro;Gabriella M.M. Paixão;Derick M. Oliveira.
Nature Communications (2020)
Complexity analysis of the marginalized particle filter
R. Karlsson;T. Schon;F. Gustafsson.
IEEE Transactions on Signal Processing (2005)
A Basic Convergence Result for Particle Filtering
Xiao-Li Hu;T.B. Schon;L. Ljung.
IEEE Transactions on Signal Processing (2008)
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