Konferenzen zum Thema Statistik, Spieltheorie in der Schweiz

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1
Statistics for Indirectly Measured Functional Data
30. Mär 2020 - 03. Apr 2020 • Lausanne, Schweiz
Veranstalter:
Bernoulli Center (CIB)
Zusammenfassung:
Functional data such as space curves, images, and surfaces are seldom sensed directly; curves are often noisily and discretely sampled, images are subject to blurring and deformation, and surfaces are often obtained via indirect linear measurements. Understanding the generation of the observations then requires modelling the act of acquisition, and solving an inverse problem in order to arrive at statistical inferences. This leads to a host of new research questions, from identifiability to trade-offs between statistical optimality and computational feasibility, which also make contact with signal processing and numerical analysis.
Themen:
Part of the Semester : Functional Data Analysis
Eintrags-ID:
1214114
2
Statistics for Topological and Discrete Data
25. Mai 2020 - 29. Mai 2020 • Lausanne, Schweiz
Veranstalter:
Bernoulli Center (CIB)
Zusammenfassung:
Topological and discrete data such as trees and networks constitute strongly non-Euclidean structures, often beyond the reach of traditional statistical tools. Their analysis is often challenged by computational tractability, as natural constraints such as permutation invariance are computationally hard to implement. Still, such data can be often transposed into more familiar contexts, whether by transformation, asymptotic arguments, or replicated observations. This workshop will explore both modelling and processing of discrete structures, also drawing in areas such as signal processing on graphs and nonparametrics.
Themen:
Part of the Semester : Functional Data Analysis
Eintrags-ID:
1214134
3
Geometrical Statistics and Functional Data
22. Jun 2020 - 26. Jun 2020 • Lausanne, Schweiz
Veranstalter:
Bernoulli Center (CIB)
Zusammenfassung:
Important classes of functional data, such as random measures and random operators, are intrinsically constrained and need to be represented as elements of suitable non-Euclidean spaces, such as Hilbert manifolds. The interplay between the infinite dimensionality of their variation and the non-linearity of their representation space poses novel challenges for statistics, at the interface of several domains, such as shape analysis, stochastic geometry, manifold learning, and functional data analysis. This workshop will bring together experts from these fields to explore these novel challenges and future perspectives.
Themen:
Part of the Semester : Functional Data Analysis
Eintrags-ID:
1214122


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Stand vom 01. April 2019