Conditional Gaussian mixture models for environmental risk mapping
Details
Serval ID
serval:BIB_BC91BEF08EAD
Type
Inproceedings: an article in a conference proceedings.
Collection
Publications
Institution
Title
Conditional Gaussian mixture models for environmental risk mapping
Title of the conference
Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing, Martigny, Switzerland
Publisher
IEEE Conference Publications
ISBN
0-7803-7616
Publication state
Published
Issued date
2002
Peer-reviewed
Oui
Pages
777 - 786
Language
english
Abstract
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.
Create date
25/11/2013 19:02
Last modification date
20/08/2019 16:30
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