•  Retrait gratuit dans votre magasin Club
  •  7.000.000 titres dans notre catalogue
  •  Payer en toute sécurité
  •  Toujours un magasin près de chez vous     
  •  Retrait gratuit dans votre magasin Club
  •  7.000.000 titres dans notre catalogue
  •  Payer en toute sécurité
  •  Toujours un magasin près de chez vous
  1. Accueil
  2. Livres
  3. Sciences humaines
  4. Psychologie
  5. Écoles & Théories de la psychologie
  6. Méthodologie
  7. Nonparametric Statistics on Manifolds and Their Applications to Object Data Analysis

Nonparametric Statistics on Manifolds and Their Applications to Object Data Analysis

Victor Patrangenaru, Leif Ellingson
Livre relié | Anglais
228,95 €
+ 457 points
Format
Livraison sous 1 à 4 semaines
Passer une commande en un clic
Payer en toute sécurité
Livraison en Belgique: 3,99 €
Livraison en magasin gratuite

Description

A New Way of Analyzing Object Data from a Nonparametric Viewpoint

Nonparametric Statistics on Manifolds and Their Applications to Object Data Analysis provides one of the first thorough treatments of the theory and methodology for analyzing data on manifolds. It also presents in-depth applications to practical problems arising in a variety of fields, including statistics, medical imaging, computer vision, pattern recognition, and bioinformatics.

The book begins with a survey of illustrative examples of object data before moving to a review of concepts from mathematical statistics, differential geometry, and topology. The authors next describe theory and methods for working on various manifolds, giving a historical perspective of concepts from mathematics and statistics. They then present problems from a wide variety of areas, including diffusion tensor imaging, similarity shape analysis, directional data analysis, and projective shape analysis for machine vision. The book concludes with a discussion of current related research and graduate-level teaching topics as well as considerations related to computational statistics.

Researchers in diverse fields must combine statistical methodology with concepts from projective geometry, differential geometry, and topology to analyze data objects arising from non-Euclidean object spaces. An expert-driven guide to this approach, this book covers the general nonparametric theory for analyzing data on manifolds, methods for working with specific spaces, and extensive applications to practical research problems. These problems show how object data analysis opens a formidable door to the realm of big data analysis.

Spécifications

Parties prenantes

Auteur(s) :
Editeur:

Contenu

Nombre de pages :
517
Langue:
Anglais

Caractéristiques

EAN:
9781439820506
Date de parution :
25-09-15
Format:
Livre relié
Format numérique:
Genaaid
Dimensions :
156 mm x 233 mm
Poids :
2299 g

Les avis