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The Pre-Kernel as a Tractable Solution for Cooperative Games, Holger Ingmar Meinhardt


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Автор: Holger Ingmar Meinhardt
Название:  The Pre-Kernel as a Tractable Solution for Cooperative Games
ISBN: 9783642395482
Издательство: Springer
Классификация:



ISBN-10: 3642395481
Обложка/Формат: Hardcover
Страницы: 242
Вес: 0.52 кг.
Дата издания: 07.11.2013
Серия: Theory and Decision Library C
Язык: English
Размер: 241 x 131 x 21
Основная тема: Economics
Подзаголовок: An Exercise in Algorithmic Game Theory
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: This book provides an alternative approach to study the pre-kernel solution of transferable utility games based on a generalized conjugation theory from convex analysis.


The Pre-Kernel as a Tractable Solution for Cooperative Games

Автор: Holger Ingmar Meinhardt
Название: The Pre-Kernel as a Tractable Solution for Cooperative Games
ISBN: 3662513145 ISBN-13(EAN): 9783662513149
Издательство: Springer
Рейтинг:
Цена: 15372.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: This book provides an alternative approach to study the pre-kernel solution of transferable utility games based on a generalized conjugation theory from convex analysis.

Reproducing Kernel Hilbert Spaces in Probability and Statistics

Автор: Alain Berlinet; Christine Thomas-Agnan
Название: Reproducing Kernel Hilbert Spaces in Probability and Statistics
ISBN: 1461347920 ISBN-13(EAN): 9781461347927
Издательство: Springer
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Цена: 30745.00 р.
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Описание: The reproducing kernel Hilbert space construction is a bijection or transform theory which associates a positive definite kernel (gaussian processes) with a Hilbert space offunctions. Like all transform theories (think Fourier), problems in one space may become transparent in the other, and optimal solutions in one space are often usefully optimal in the other. The theory was born in complex function theory, abstracted and then accidently injected into Statistics; Manny Parzen as a graduate student at Berkeley was given a strip of paper containing his qualifying exam problem- It read "reproducing kernel Hilbert space"- In the 1950's this was a truly obscure topic. Parzen tracked it down and internalized the subject. Soon after, he applied it to problems with the following fla- vor: consider estimating the mean functions of a gaussian process. The mean functions which cannot be distinguished with probability one are precisely the functions in the Hilbert space associated to the covariance kernel of the processes. Parzen's own lively account of his work on re- producing kernels is charmingly told in his interview with H. Joseph Newton in Statistical Science, 17, 2002, p. 364-366. Parzen moved to Stanford and his infectious enthusiasm caught Jerry Sacks, Don Ylvisaker and Grace Wahba among others. Sacks and Ylvis- aker applied the ideas to design problems such as the following. Sup- pose (XdO


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