High Dimensional Probability VI
The Banff Volume
Houdré, Christian.
editor.
Mason, David M.
editor.
Rosiński, Jan.
editor.
Wellner, Jon A.
editor.
SpringerLink (Online service)
text
sz
2013
monographic
eng
access
XIII, 374 p. online resource.
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. 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. 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.
edited by Christian Houdré, David M. Mason, Jan Rosiński, Jon A. Wellner.
Mathematics
Computer science
Mathematics
Computer mathematics
Calculus of variations
Probabilities
Mathematics
Probability Theory and Stochastic Processes
Mathematical Applications in Computer Science
Calculus of Variations and Optimal Control; Optimization
QA273.A1-274.9
QA274-274.9
519.2
Springer eBooks
Progress in Probability ; 66
9783034804905
http://dx.doi.org/10.1007/978-3-0348-0490-5
http://dx.doi.org/10.1007/978-3-0348-0490-5
130420
20160413122336.0
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