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Summary: LETTER Communicated by Steven Nowlan
Neural Network Uncertainty Assessment Using Bayesian
Statistics: A Remote Sensing Application
F. Aires
faires@giss.nasa.gov
Department of Applied Physics and Applied Mathematics, Columbia University, NASA
Goddard Institute for Space Studies, New York, NY 10025, U.S.A., and CNRS/IPSL/
Laboratoire de M´et´eorologie Dynamique, ´Ecole Polytechnique, 91128 Palaiseau Cedex,
France
C. Prigent
catherine.prigent@obspm.fr
CNRS, LERMA, Observatoire de Paris, Paris 75014, France
W.B. Rossow
wrossow@giss.nasa.gov
NASA Goddard Institute for Space Studies, New York, NY 10025, U.S.A.
Neural network (NN) techniques have proved successful for many re-
gression problems, in particular for remote sensing; however, uncertainty
estimates are rarely provided. In this article, a Bayesian technique to eval-
uate uncertainties of the NN parameters (i.e., synaptic weights) is first
presented. In contrast to more traditional approaches based on point es-
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