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High Dimensional Probability VI


High Dimensional Probability VI

The Banff Volume
Progress in Probability, Band 66

von: Christian Houdré, David M. Mason, Jan Rosinski, Jon A. Wellner

149,79 €

Verlag: Birkhäuser
Format: PDF
Veröffentl.: 19.04.2013
ISBN/EAN: 9783034804905
Sprache: englisch
Anzahl Seiten: 373

Dieses eBook enthält ein Wasserzeichen.

Beschreibungen

<p>This is a collection of papers by participants at High Dimensional Probability VI Meeting held from October 9-14, 2011 at the Banff International Research Station in Banff, Alberta, Canada. </p><p>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 areas of mathematics, statistics, and computer science. These include random matrix theory, nonparametric statistics, empirical process theory, statistical learning theory, concentration of measure phenomena, strong and weak approximations, distribution function estimation in high dimensions, combinatorial optimization, and random graph theory. </p><p>The papers in this volume show that HDP theory continues to develop new tools, methods, techniques and perspectives to analyze the random phenomena. Both researchers and advanced students will find this book of great use for learning about new avenues of research.​</p>
<p>This is a collection of papers by participants at the High Dimensional Probability VI Meeting held from October 9-14, 2011 at the Banff International Research Station in Banff, Alberta, Canada. </p><p>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 areas of mathematics, statistics, and computer science. These include random matrix theory, nonparametric statistics, empirical process theory, statistical learning theory, concentration of measure phenomena, strong and weak approximations, distribution function estimation in high dimensions, combinatorial optimization, and random graph theory. </p>The papers in this volume show that HDP theory continues to develop new tools, methods, techniques and perspectives to analyze the random phenomena. Both researchers and advanced students will find this book of great use for learning about new avenues of research.​
Gives a unique view on the mathematical methods used by experts to establish limit theorems in probability and statistics, which reside in high dimensions Displays the wide scope of the types of problems to which these methods can be successfully applied Provides a valuable introduction to what is meant by high dimensional probability and exposes fruitful new areas of research in the area

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