Titlebook: Computational Information Geometry; For Image and Signal Frank Nielsen,Frank Critchley,Christopher T. J. Do Book 2017 Springer Internationa

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書(shū)目名稱(chēng)Computational Information Geometry影響因子(影響力)




書(shū)目名稱(chēng)Computational Information Geometry影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)Computational Information Geometry網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)Computational Information Geometry網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Computational Information Geometry被引頻次




書(shū)目名稱(chēng)Computational Information Geometry被引頻次學(xué)科排名




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書(shū)目名稱(chēng)Computational Information Geometry讀者反饋




書(shū)目名稱(chēng)Computational Information Geometry讀者反饋學(xué)科排名

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On the Geometric Interplay Between Goodness-of-Fit and Estimation: Illustrative Examples,e. A geometric analysis of simple, yet representative, models involving the same population parameter compellingly establishes the main theme of the paper: namely, that goodness-of-fit is necessary but not sufficient for model selection. Visual examples vividly communicate this. Specifically, for a

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,Extrinsic Projection of It? SDEs on Submanifolds with Applications to Non-linear Filtering,evelop low dimensional approximations to high dimensional SDEs in a differential geometric setting. We consider the example of approximating the non-linear filtering problem with a Gaussian distribution and show how the It? projection leads to improved approximations in the Gaussian family. We brief

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,Fast ,-Approximation of the L?wner Extremal Matrices of High-Dimensional Symmetric Matrices,sets of 3D symmetric positive definite matrices anchored at voxel positions capturing the anisotropic diffusion properties of water molecules in biological tissues. The space of symmetric matrices can be partially ordered using the L?wner ordering, and computing extremal matrices dominating a given

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Dimensionality Reduction for Information Geometric Characterization of Surface Topographies,synthetic bone to ocean wave height distributions and cosmic phenomena like inter-galactic cluster void distributions. Here we used a data set of 35 surface topographies, each of . pixels with spatial resolution between 4 and 7?.m per pixel, and fitted trivariate Gaussian distributions to represent

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On Clustering Financial Time Series: A Need for Distances Between Dependent Random Variables, for image and signal processing. This workshop brought several experts in pure and applied mathematics together with applied researchers from medical imaging, radar signal processing and finance. The authors belong to the latter group. This document was written as a long introduction to further dev

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Dimensionality Reduction for Measure Valued Evolution Equations in Statistical Manifolds,l equation or the Kushner–Stratonovich resp. Duncan–Mortensen–Zakai stochastic partial differential equations of nonlinear filtering, with potential applications to signal processing, quantitative finance, heat flows and quantum theory among many other areas. Our method is based on the projection co