Algebraic and Geometric Methods in Statistics

Algebraic and Geometric Methods in Statistics
Author: Paolo Gibilisco
Publisher: Cambridge University Press
Total Pages: 447
Release: 2010
Genre: Mathematics
ISBN: 0521896193

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An up-to-date account of algebraic statistics and information geometry, which also explores the emerging connections between these two disciplines.


Algebraic and Geometric Methods in Statistics
Language: en
Pages: 447
Authors: Paolo Gibilisco
Categories: Mathematics
Type: BOOK - Published: 2010 - Publisher: Cambridge University Press

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An up-to-date account of algebraic statistics and information geometry, which also explores the emerging connections between these two disciplines.
Geometric Methods in Algebra and Number Theory
Language: en
Pages: 365
Authors: Fedor Bogomolov
Categories: Mathematics
Type: BOOK - Published: 2006-06-22 - Publisher: Springer Science & Business Media

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* Contains a selection of articles exploring geometric approaches to problems in algebra, algebraic geometry and number theory * The collection gives a represen
Differential-Geometrical Methods in Statistics
Language: en
Pages: 302
Authors: Shun-ichi Amari
Categories: Mathematics
Type: BOOK - Published: 2012-12-06 - Publisher: Springer Science & Business Media

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From the reviews: "In this Lecture Note volume the author describes his differential-geometric approach to parametrical statistical problems summarizing the res
Polyhedral and Algebraic Methods in Computational Geometry
Language: en
Pages: 251
Authors: Michael Joswig
Categories: Mathematics
Type: BOOK - Published: 2013-01-04 - Publisher: Springer Science & Business Media

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Polyhedral and Algebraic Methods in Computational Geometry provides a thorough introduction into algorithmic geometry and its applications. It presents its prim
Algebraic Geometry and Statistical Learning Theory
Language: en
Pages: 295
Authors: Sumio Watanabe
Categories: Computers
Type: BOOK - Published: 2009-08-13 - Publisher: Cambridge University Press

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Sure to be influential, Watanabe's book lays the foundations for the use of algebraic geometry in statistical learning theory. Many models/machines are singular