Assigning Confidence Intervals to Neural Network Predictions

@inproceedings{Dybowski1997AssigningCI,
  title={Assigning Confidence Intervals to Neural Network Predictions},
  author={Richard Dybowski},
  year={1997}
}
Abstract This report reviews three possible approaches to the assignment of confidence intervals to feed-forward neural networks, namely, bootstrap estimation, maximum likelihood estimation, and Bayesian statistics. The report concludes with a proposal for mixture modelling via Markov Chain Monte Carlo sampling to enable non-Gaussian variances to be modelled without introducing the bias caused by maximum likelihood. 

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