Conditional Gaussian mixture models for environmental risk mapping
Détails
ID Serval
serval:BIB_BC91BEF08EAD
Type
Actes de conférence (partie): contribution originale à la littérature scientifique, publiée à l'occasion de conférences scientifiques, dans un ouvrage de compte-rendu (proceedings), ou dans l'édition spéciale d'un journal reconnu (conference proceedings).
Collection
Publications
Institution
Titre
Conditional Gaussian mixture models for environmental risk mapping
Titre de la conférence
Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing, Martigny, Switzerland
Editeur
IEEE Conference Publications
ISBN
0-7803-7616
Statut éditorial
Publié
Date de publication
2002
Peer-reviewed
Oui
Pages
777 - 786
Langue
anglais
Résumé
This paper proposes the use of Gaussian mixture models to estimate
conditional probability density functions in an environmental risk
mapping context. A conditional Gaussian mixture model has been compared
to, the geostatistical method of sequential Gaussian simulations
and shows good performance in reconstructing the local PDF. The data
sets used for this comparison are parts of the digital elevation
model of Switzerland.
conditional probability density functions in an environmental risk
mapping context. A conditional Gaussian mixture model has been compared
to, the geostatistical method of sequential Gaussian simulations
and shows good performance in reconstructing the local PDF. The data
sets used for this comparison are parts of the digital elevation
model of Switzerland.
Création de la notice
25/11/2013 18:02
Dernière modification de la notice
20/08/2019 15:30
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