Publication Abstracts

Aires et al. 2004

Aires, F., C. Prigent, and W.B. Rossow, 2004: Neural network uncertainty assessment using Bayesian statistics with application to remote sensing: 2. Output errors. J. Geophys. Res., 109, D10304, doi:10.1029/2003JD004174.

A technique to estimate the uncertainties of the parameters of a neural network model, i.e., the synaptic weights, was described in companion paper 1. Using these weight uncertainty estimates, we compute the uncertainties in the network outputs (i.e., error bars and correlation structure of these errors). Such quantities are very important for evaluating any application of the neural network technique. The theory is applied to the same remote sensing problem as in companion paper 1 concerning the retrieval of surface skin temperature, microwave surface emissivities and integrated water vapor content from a combined analysis of microwave and infrared observations over land.

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BibTeX Citation

@article{ai01100t,
  author={Aires, F. and Prigent, C. and Rossow, W. B.},
  title={Neural network uncertainty assessment using Bayesian statistics with application to remote sensing: 2. Output errors},
  year={2004},
  journal={J. Geophys. Res.},
  volume={109},
  pages={D10304},
  doi={10.1029/2003JD004174},
}

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RIS Citation

TY  - JOUR
ID  - ai01100t
AU  - Aires, F.
AU  - Prigent, C.
AU  - Rossow, W. B.
PY  - 2004
TI  - Neural network uncertainty assessment using Bayesian statistics with application to remote sensing: 2. Output errors
JA  - J. Geophys. Res.
VL  - 109
SP  - D10304
DO  - 10.1029/2003JD004174
ER  -

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