High Dimensional Probability VII

Christian Houdré

ISBN 10: 3319821210 / ISBN 13: 9783319821214

Editore: Springer International Publishing, Springer International Publishing Jun 2018, 2018

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Neuware -This volume collects selected papers from the 7th High Dimensional Probability meeting held at the Institut d'Études Scientifiques de Cargèse (IESC) in Corsica, France.High Dimensional Probability (HDP) is an area of mathematics that includes the study of probability distributions and limit theorems in infinite-dimensional spaces such as Hilbert spaces and Banach spaces. The most remarkable feature of this area is that it has resulted in the creation of powerful new tools and perspectives, whose range of application has led to interactions with other subfields of mathematics, statistics, and computer science. These include random matrices, nonparametric statistics, empirical processes, statistical learning theory, concentration of measure phenomena, strong and weak approximations, functional estimation, combinatorial optimization, and random graphs.The contributions in this volume show that HDP theory continues to thrive and develop new tools, methods, techniques andperspectives to analyze random phenomena.Springer Basel AG in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin 492 pp. Englisch.

Codice articolo 9783319821214

Dati bibliografici

• Titolo: High Dimensional Probability VII
• Editore: Springer International Publishing, Springer International Publishing Jun 2018
• Data di pubblicazione: 2018
• Lingua: Inglese
• ISBN 10: 3319821210
• ISBN 13: 9783319821214
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 155 A x 27 P (millimetri) 235 millimetri di larghezza per 155 millimetri di altezza per 27 millimetri di profondità
• Peso: 739 grammi

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Riassunto

This volume collects selected papers from the 7th High Dimensional Probability meeting held at the Institut d'Études Scientifiques de Cargèse (IESC) in Corsica, France.

High Dimensional Probability (HDP) is an area of mathematics that includes the study of probability distributions and limit theorems in infinite-dimensional spaces such as Hilbert spaces and Banach spaces. The most remarkable feature of this area is that it has resulted in the creation of powerful new tools and perspectives, whose range of application has led to interactions with other subfields of mathematics, statistics, and computer science. These include random matrices, nonparametric statistics, empirical processes, statistical learning theory, concentration of measure phenomena, strong and weak approximations, functional estimation, combinatorial optimization, and random graphs.

The contributions in this volume show that HDP theory continues to thrive and develop new tools, methods, techniques andperspectives to analyze random phenomena.