Sauter Tobias (15 results)

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Paperback. Condition: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.

Broschiert, dies sind regulär ausgesonderte Exemplare aus einer wissenschaftlichen Bibliothek, keine Markierungen/Anmerkungen, die Bücher sind gut erhalten. Shipping to abroad insured with tracking number.

paperback. Condition: Ottimo (Fine). Katalog zur Ausstellung, Basel, september-october 1991. Eisen als sache und idee von Aurel Schmidt. Iron as object and idea by Aurel Achmidt. Biographische angaben, einzelausstellungen, gruppenausstellungen, preise . 8vo. pp. 12. . Ottimo (Fine). . . . Book.

Soft cover. Condition: Good. 1st Edition. 8° Ob. - un-paginated - Dual-tone photo-reproductions. Exhibition catalog, text in German language. Original boards. In Very good condition.

Soft cover. Condition: Very Good. 1st Edition. 8° oblong - 16 pp - Catalogue from the artist's exhibition in Basel. Drawings and reproductions from his ouevre in Brasil and Switzerland. Text in german.

Taschenbuch. Condition: Neu. Non-parametric modelling in Geoscience | Application, optimization and uncertainty estimation | Tobias Sauter | Taschenbuch | 160 S. | Englisch | 2015 | Südwestdeutscher Verlag für Hochschulschriften | EAN 9783838131061 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Broschiert. VII, 416 S. 2 Bände. Broschur. Rücken minimal aufgehellt, gute Exemplare. Band I: Judäa - Saman Band II: Ataman - Jazz.

zus. VII, 856 S. Sprache: Deutsch Gewicht in Gramm: 1100 8°. Original-kartoniert, Bibliotheksexemplare mit Rückenschild, Signaturen und Stempeln (entwidmet), Rücken verfärbt und knickspurig, Einbände und Schnitte teils fleckig, Ecken und Kanten etwas bestoßen, insgesamt ordentlich erhalten.

Condition: Sehr gut. Zustand: Sehr gut | Produktart: Bücher | Keine Beschreibung verfügbar.

Condition: Gut. Gebraucht - Gut ** ehemal. Bibliotheks-Exemplare. gute, saubere Exemplare, Keine Markierungen im Text **.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In Geosciences inferences on processes are usually derived from irregularly measured data in both space and time. Such empirical time series are often characterized by changing boundary conditions, nonlinearity and uncertainty. Hence, phenomenological knowledge of processes is indispensable in order to derive physically meaningful equations describing the underlying dynamical system. However, unambiguous inferences prove to be difficult when nonlinearity and non-stationarity are present. In contrast to numerical models, data-driven models are specifically built to be parsimonious with a minimal set of adjustable parameters, intended to reproduce the statistical properties of signals. This dissertation focuses on the fundamental aspects of uncertainty estimation of nonlinear data-driven prediction methods. Within this framework, the essential factors in the model development process are emphasized and discussed on the basis of both climatological and hydrological time series. A key issue of the thesis is whether the analysis of uncertainties might contribute to process understanding and eventually support the optimization of data-driven models. 160 pp. Englisch.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Sauter TobiasThe author was born 1977 in Aalen, Germany. He received his PhD in Physical Geography from the RWTH Aachen University in 2011. Currently he holds a postdoc position at the Department of Geography ,RWTH University (Physic.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -In Geosciences inferences on processes are usually derived from irregularly measured data in both space and time. Such empirical time series are often characterized by changing boundary conditions, nonlinearity and uncertainty. Hence, phenomenological knowledge of processes is indispensable in order to derive physically meaningful equations describing the underlying dynamical system. However, unambiguous inferences prove to be difficult when nonlinearity and non-stationarity are present. In contrast to numerical models, data-driven models are specifically built to be parsimonious with a minimal set of adjustable parameters, intended to reproduce the statistical properties of signals. This dissertation focuses on the fundamental aspects of uncertainty estimation of nonlinear data-driven prediction methods. Within this framework, the essential factors in the model development process are emphasized and discussed on the basis of both climatological and hydrological time series. A key issue of the thesis is whether the analysis of uncertainties might contribute to process understanding and eventually support the optimization of data-driven models.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 160 pp. Englisch.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In Geosciences inferences on processes are usually derived from irregularly measured data in both space and time. Such empirical time series are often characterized by changing boundary conditions, nonlinearity and uncertainty. Hence, phenomenological knowledge of processes is indispensable in order to derive physically meaningful equations describing the underlying dynamical system. However, unambiguous inferences prove to be difficult when nonlinearity and non-stationarity are present. In contrast to numerical models, data-driven models are specifically built to be parsimonious with a minimal set of adjustable parameters, intended to reproduce the statistical properties of signals. This dissertation focuses on the fundamental aspects of uncertainty estimation of nonlinear data-driven prediction methods. Within this framework, the essential factors in the model development process are emphasized and discussed on the basis of both climatological and hydrological time series. A key issue of the thesis is whether the analysis of uncertainties might contribute to process understanding and eventually support the optimization of data-driven models.