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1	Approximations and Applications of Nonlinear Filters / Approximation und Anwendung nichtlinearer Filter Bröcker, Jochen 30 January 2003 (has links) No description available. 530 Physik Mathematics and Computer Science Nichtlineare Filterung Dynamische Systeme Sequenzielle Datenerfassung Endlich Dimensionale Filter Kullback-Leibler Abstand Hellinger Abstand Totale Variation Exponentielle Familien Lineare Familien Monte-Carlo Filter kommunikationssysteme Parameterschätzung nonlinear filtering dynamical systems sequencial data aquisition finite dimensional filter Kullback-Leibler distance Hellinger distance total variation distance exponential families linear families Monte Carlo filter communication systems parameter estimation 31.73 E
2	Studies on two specific inverse problems from imaging and finance Rückert, Nadja 20 July 2012 (has links) (PDF) This thesis deals with regularization parameter selection methods in the context of Tikhonov-type regularization with Poisson distributed data, in particular the reconstruction of images, as well as with the identiﬁcation of the volatility surface from observed option prices. In Part I we examine the choice of the regularization parameter when reconstructing an image, which is disturbed by Poisson noise, with Tikhonov-type regularization. This type of regularization is a generalization of the classical Tikhonov regularization in the Banach space setting and often called variational regularization. After a general consideration of Tikhonov-type regularization for data corrupted by Poisson noise, we examine the methods for choosing the regularization parameter numerically on the basis of two test images and real PET data. In Part II we consider the estimation of the volatility function from observed call option prices with the explicit formula which has been derived by Dupire using the Black-Scholes partial diﬀerential equation. The option prices are only available as discrete noisy observations so that the main diﬃculty is the ill-posedness of the numerical diﬀerentiation. Finite diﬀerence schemes, as regularization by discretization of the inverse and ill-posed problem, do not overcome these diﬃculties when they are used to evaluate the partial derivatives. Therefore we construct an alternative algorithm based on the weak formulation of the dual Black-Scholes partial diﬀerential equation and evaluate the performance of the ﬁnite diﬀerence schemes and the new algorithm for synthetic and real option prices. Tikhonov-Regularisierung Kullback-Leibler Totale Variation Poissonverteilte Daten Bildverarbeitung Wahl des Regularisierungsparameters Diskrepanzprinzip L-Kurve Quasi-Optimalitätskriterium PET Black-Scholes-Modell Volatilität Dupire Call-Option Regularisierung durch Diskretisierung Parameter Identifikationsalgorithmus Tikhonov-Regularization Kullback-Leibler Total Variation Poisson distributed data discrepancy principle L-curve method quasi-optimality criterion PET Black-Scholes model volatility Dupire call option regularization by discretization imaging finance ddc:515 ddc:519 Tichonov-Regularisierung Black-Scholes-Modell Volatilität Regularisierungsverfahren Poisson-Verteilung Bildverarbeitung Call-Option Parameter <Mathematik>
3	Studies on two specific inverse problems from imaging and finance Rückert, Nadja 16 July 2012 (has links) This thesis deals with regularization parameter selection methods in the context of Tikhonov-type regularization with Poisson distributed data, in particular the reconstruction of images, as well as with the identiﬁcation of the volatility surface from observed option prices. In Part I we examine the choice of the regularization parameter when reconstructing an image, which is disturbed by Poisson noise, with Tikhonov-type regularization. This type of regularization is a generalization of the classical Tikhonov regularization in the Banach space setting and often called variational regularization. After a general consideration of Tikhonov-type regularization for data corrupted by Poisson noise, we examine the methods for choosing the regularization parameter numerically on the basis of two test images and real PET data. In Part II we consider the estimation of the volatility function from observed call option prices with the explicit formula which has been derived by Dupire using the Black-Scholes partial diﬀerential equation. The option prices are only available as discrete noisy observations so that the main diﬃculty is the ill-posedness of the numerical diﬀerentiation. Finite diﬀerence schemes, as regularization by discretization of the inverse and ill-posed problem, do not overcome these diﬃculties when they are used to evaluate the partial derivatives. Therefore we construct an alternative algorithm based on the weak formulation of the dual Black-Scholes partial diﬀerential equation and evaluate the performance of the ﬁnite diﬀerence schemes and the new algorithm for synthetic and real option prices. info:eu-repo/classification/ddc/515 ddc:515 info:eu-repo/classification/ddc/519 ddc:519

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