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Project

Project type/Form of grant
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Title [sv]
Probabilistisk modellering av dynamiska system
Title [en]
Probabilistic modeling of dynamical systems
Abstract [sv]
The amount of available data is rapidly increasing and to bring out useful information from this data, it has to be modeled and analyzed. A model is a compact and interpretable representation of the data. We will provide new models together with new algorithms, allowing us to automatically learn these models from data generated by the ever-present dynamical systems.We use probabilistic models, allowing us to employ powerful and well-developed probability theory to represent and systematically work with the uncertainty that is inherent in most data. Our new models are capable of expressing uncertainty, not only over parameters, but also over structural aspects of the models, such as segmentations and model orders. These models are based on relatively new combinatorial stochastic processes emerging from the rapidly developing Bayesian nonparametric theory. This opens up for a powerful foundation to model complex dynamical systems, where the structure of the system is changing over time (sometimes referred to as switching or hybrid systems). However, as we seek more flexible models, the inference problems become increasingly more difficult. During the last five years we have witnessed (and contributed to) the development of a very powerful class of inference algorithms, constituted by an elegant mix of particle filtering and Markov chain Monte Carlo methods. This provides us with the mathematical basis we need to tackle the challenging inference problems in this project.
Principal InvestigatorSchön, Thomas
Coordinating organisation
Uppsala University
Funder
Period
2014-01-01 － 2017-12-31
National Category
Control EngineeringSignal Processing
Identifiers
DiVA, id: project:4984Project, id: 2013-05524_VR

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