Titlebook: Stochastic Programming; Numerical Techniques Kurt Marti,Peter Kall Book 1995 Springer-Verlag Berlin Heidelberg 1995 Stochastische Optimieru

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K. Bagi,A. Vásárhelyie prediction of an unknown value of study variable corresponding to a known value of explanatory variable. Usually, when the least square estimators are used to construct the predictors, they yield the best linear unbiased predictors provided the data recorded on variables is measured without any er

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Jitka Dupačová,Pavel Charamza,Jan Mádl only the remaining units is a popular, easy to implement approach in this case. This can possibly introduce severe bias if the strong assumption of a missing pattern that is completely at random (MCAR) is not fulfilled (see for example Rubin (1987)). Imputing the missing values can overcome this pr

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K. Marti,S. Qu alkyl chains. The liquid crystalline properties of these materials are found to depend critically on the length of the alkyl chain. For example, both the nematic-isotropic transition temperature and the entropy of transition exhibit a marked alternation with the number of methylene groups in the fl

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T. Szántai,I. Mészáros,J. Völgyilustrated the effectiveness of convolutional neural networks (CNNs) trained on simulated or synthetic images for detecting objects in real-world images. Synthesized training images with automatically generated annotations at the object-level offer a promising alternative to the laborious and costly

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https://doi.org/10.1007/978-3-642-88272-2Stochastische Optimierung; Unternehmensforschung; linear optimization; modeling; numerical methods; opera