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Titlebook: Modeling Complex Data for Creating Information; Jacques-Emile Dubois,Nahum Gershon Book 1996 Springer-Verlag Berlin Heidelberg 1996 Access

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樓主: 契約
21#
發(fā)表于 2025-3-25 05:58:07 | 只看該作者
lations for various coordinate values, one can explore multidimensional energy surfaces. These energy surfaces are the basis for molecular dynamics and Monte Carlo studies. Another important method for exploring these energy surfaces is to find configurations for which the energy is a minimum. By th
22#
發(fā)表于 2025-3-25 10:30:58 | 只看該作者
23#
發(fā)表于 2025-3-25 15:07:02 | 只看該作者
Designing Robust SCNs Under Risk,g horizons under risk is examined. Then, the concepts of responsiveness, resilience, and robustness in an SCN design context are discussed. Subsequently, basic stochastic programming notions are introduced. The sample average approximation (SAA) method and coherent risk measures are explained and th
24#
發(fā)表于 2025-3-25 19:06:45 | 只看該作者
25#
發(fā)表于 2025-3-25 22:53:31 | 只看該作者
On Hidden Reason for Fractals from Water,pecially water stores its molecules in a series. The chirality of water molecules can allow vortex generation only in counter clockwise direction. If both these properties are combined, any viscoelastic materials like elastomers added in water and undergoes the forced vortex will turn these material
26#
發(fā)表于 2025-3-26 00:12:02 | 只看該作者
27#
發(fā)表于 2025-3-26 07:10:37 | 只看該作者
C++ Toolkit for Engineers and Scientistshe full-likelihood expectation. So a novel approach for multi-user detection based on the ECM iterative algorithm is proposed. Compared with the EM algorithm, the ECM algorithm reduces the computational complexity of the M-step. The results show that the proposed algorithm has well performance and Convergence in Gaussian noise.
28#
發(fā)表于 2025-3-26 09:18:52 | 只看該作者
29#
發(fā)表于 2025-3-26 13:20:38 | 只看該作者
30#
發(fā)表于 2025-3-26 18:14:45 | 只看該作者
Jurijs Spiridonovs,Olga Bogdanovaissing. Evaluation of algorithms was done by measuring the accuracy of the classification model previously trained on the uncorrupted dataset. The results show that GAIN and especially WGAIN are the best imputers regardless of the conditions. In general, they outperform or are comparative to MICE, .
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