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DTSTAMP:20190117T155824
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SUMMARY:Talk of Prof. Lenka Pavelková
DESCRIPTION:Prof. Lenka Pavelková\nInstitute of Information Theory and Automation\n Prague, Czech Republic  \nTuesday 2015-11-03 16:00\n IST-Seminar-Room V2.268 - Pfaffenwaldring 9 - Campus Stuttgart-Vaihingen  \nAbstract\nRecursive estimation is an important part of the decision making tasks such as prediction and\nadaptive control. The system in question is often modelled by an autoregressive model with\nexogenous inputs (ARX). Uncertainty of this model is mostly assumed to be normal and the estimation\nof unknown model parameters is based on least squares methods. A state space model is another\nfrequently used model. Considering normal noises in this model, Kalman filtering is standardly used\nfor the states estimation. Nevertheless, the Gaussian models do not respect possible strict\nboundaries of involved model variables. In such cases, the above mentioned estimation procedures\nhave to be additionally adapted.\nIn this talk, the mentioned estimation problem with constraints will be addressed\nstraightforwardly within the Bayesian approach. The results of the research of both ARX and state\nspace models with uniformly distributed noises will be presented. An extension towards other\nbounded distributions will be mentioned. Applications in the transportation area and industry will\nbe shown.\nBiographical Information\nLenka Pavelková graduated in Technical Cybernetics at the Czech Technical University in Prague,\nCzech Republic, in 1992, and obtained her Ph.D. in Applied Mathematics in 2009 from the same\nuniversity. Currently, she works as a research worker at the Department of Adaptive systems,\nInstitute of Information Theory and Automation, Czech Academy of Sciences, Prague. Her research\ninterests are in the field of Bayesian decision making. She focuses mainly on the estimation of\nconstrained systems.
DTSTART;TZID=Europe/Berlin;VALUE=DATE:20151103
URL;VALUE=URI:https://www.ist.uni-stuttgart.de/events/Talk-of-Prof.-Lenka-Pavelkova/
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