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Functional data and their principal components analysis
Kasanický, Ivan ; Hlubinka, Daniel (advisor) ; Hušková, Marie (referee)
Presented thesis deals with analysis of functional data. In the first part, problem which arises because of only finite possible numbers of observations is discussed. This problem is solved using representation by basis functions with emphasis on B-splines basis. The second part is focused on functional principal component analysis that could be understood as a natural extension of a multivariate case or as an application of Karhunen-Lo`eve expansion , which is based on Mercer's theorem. Estimations of principal components together with rates of convergence are mentioned too. Practical computation of principal components is mentioned in the last chapter.
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Ensemble Kalman filter on high and infinite dimensional spaces
Kasanický, Ivan ; Hlubinka, Daniel (advisor) ; Pannekoucke, Olivier (referee) ; Antoch, Jaromír (referee)
Title: Ensemble Kalman filter on high and infinite dimensional spaces Author: Mgr. Ivan Kasanický Department: Department of Probability and Mathematical Statistics Supervisor: doc. RNDr. Daniel Hlubinka, Ph.D., Department of Probability and Mathematical Statistics Consultant: prof. RNDr. Jan Mandel, CSc., Department of Mathematical and Statistical Sciences, University of Colorado Denver Abstract: The ensemble Kalman filter (EnKF) is a recursive filter, which is used in a data assimilation to produce sequential estimates of states of a hidden dynamical system. The evolution of the system is usually governed by a set of di↵erential equations, so one concrete state of the system is, in fact, an element of an infinite dimensional space. In the presented thesis we show that the EnKF is well defined on a infinite dimensional separable Hilbert space if a data noise is a weak random variable with a covariance bounded from below. We also show that this condition is su cient for the 3DVAR and the Bayesian filtering to be well posed. Additionally, we extend the already known fact that the EnKF converges to the Kalman filter in a finite dimension, and prove that a similar statement holds even in a infinite dimension. The EnKF su↵ers from a low rank approximation of a state covariance, so a covariance localization is required in...
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Functional data and their principal components analysis
Kasanický, Ivan ; Hlubinka, Daniel (advisor) ; Hušková, Marie (referee)
Presented thesis deals with analysis of functional data. In the first part, problem which arises because of only finite possible numbers of observations is discussed. This problem is solved using representation by basis functions with emphasis on B-splines basis. The second part is focused on functional principal component analysis that could be understood as a natural extension of a multivariate case or as an application of Karhunen-Lo`eve expansion , which is based on Mercer's theorem. Estimations of principal components together with rates of convergence are mentioned too. Practical computation of principal components is mentioned in the last chapter.
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Popis modelu TDD, verze 3.5
Konár, Ondřej ; Brabec, Marek ; Kasanický, Ivan ; Malý, Marek ; Pelikán, Emil
Zpráva obsahuje popis tvorby a použití modelu TDD pro odhad spotřeby zemního plynu zákazníků s měřením typu C. Součástí zprávy je metodika použití modelu TDD operátorem trhu, dále metodika použití TDD provozovatelem distribuční soustavy (PDS), popis aktualizace modelu TDD a popis předávaných souborů s parametry. Model je otestován na reálných datech ze zákaznického kmene distribuèní spoleènosti RWE GasNet a na datech z mimořádných průběhových měření. Dokument zahrnuje stav ke dni 15.10.2014.
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Eulerovské chemické transportní modely, jejich výhody a možnosti využití
Resler, Jaroslav ; Karel, J. ; Jireš, R. ; Liczki, Jitka ; Belda, Michal ; Eben, Kryštof ; Kasanický, Ivan ; Juruš, Pavel ; Vlček, O. ; Benešová, N. ; Kazmuková, M.
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