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Titlebook: An Invitation to Statistics in Wasserstein Space; Victor M. Panaretos,Yoav Zemel Book‘‘‘‘‘‘‘‘ 2020 The Editor(s) (if applicable) and The A

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發(fā)表于 2025-3-21 18:52:23 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱An Invitation to Statistics in Wasserstein Space
影響因子2023Victor M. Panaretos,Yoav Zemel
視頻videohttp://file.papertrans.cn/156/155649/155649.mp4
發(fā)行地址Gives a succinct introduction to necessary mathematical background, focusing on the results useful for statistics from an otherwise vast mathematical literature.Presents an up-to-date overview of the
學(xué)科分類SpringerBriefs in Probability and Mathematical Statistics
圖書封面Titlebook: An Invitation to Statistics in Wasserstein Space;  Victor M. Panaretos,Yoav Zemel Book‘‘‘‘‘‘‘‘ 2020 The Editor(s) (if applicable) and The A
影響因子.This open access book presents the key aspects of statistics in Wasserstein spaces, i.e. statistics in the space of probability measures when endowed with the geometry of optimal transportation. Further to reviewing state-of-the-art aspects, it?also provides an?accessible introduction to the fundamentals of this current topic, as well as an overview that?will serve as an invitation and catalyst for further research.. . Statistics in Wasserstein spaces represents an emerging topic in?mathematical statistics, situated at the interface between functional data?analysis (where the data are functions, thus lying in infinite dimensional?Hilbert space) and non-Euclidean statistics (where the data satisfy nonlinear?constraints, thus lying on non-Euclidean manifolds). The Wasserstein?space provides the natural mathematical formalism to describe data?collections that are best modeled as random measures on Euclidean space (e.g. images?and point processes). Such random measures carry the infinite dimensional?traits of functional data, but are intrinsically nonlinear due to positivity and?integrability restrictions. Indeed, their dominating statistical variation?arises through random deformatio
Pindex Book‘‘‘‘‘‘‘‘ 2020
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發(fā)表于 2025-3-21 23:29:22 | 只看該作者
板凳
發(fā)表于 2025-3-22 04:20:11 | 只看該作者
,Der Petroleum-Tankdampfer ?August Korff?,stein space .. It reduces the problem of finding the Fréchet mean to a succession of pairwise transport problems, involving only the Monge–Kantorovich problem between two measures. In the Gaussian case (or any location-scatter family), the latter can be done explicitly, rendering the algorithm particularly appealing (see Sect. 5.4.1).
地板
發(fā)表于 2025-3-22 08:19:21 | 只看該作者
The Wasserstein Space,r some basic definitions, we describe the topological properties of that space in Sect. 2.2. It is then explained in Sect. 2.3 how . can be endowed with a sort of infinite-dimensional Riemannian structure. Measurability issues are dealt with in the somewhat technical Sect. 2.4.
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發(fā)表于 2025-3-22 09:56:22 | 只看該作者
,Construction of Fréchet Means and Multicouplings,stein space .. It reduces the problem of finding the Fréchet mean to a succession of pairwise transport problems, involving only the Monge–Kantorovich problem between two measures. In the Gaussian case (or any location-scatter family), the latter can be done explicitly, rendering the algorithm particularly appealing (see Sect. 5.4.1).
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發(fā)表于 2025-3-22 13:36:18 | 只看該作者
2365-4333 hematical literature.Presents an up-to-date overview of the .This open access book presents the key aspects of statistics in Wasserstein spaces, i.e. statistics in the space of probability measures when endowed with the geometry of optimal transportation. Further to reviewing state-of-the-art aspect
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發(fā)表于 2025-3-22 18:29:45 | 只看該作者
https://doi.org/10.1007/978-3-030-38438-8Optimal Transportation; Monge-Kantorovich Problem; Barycenter; Multimarginal Transport; Functional Data
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