Neural networks and the bias/variance dilemma
Neural Computation
Neural Computation
A practical Bayesian framework for backpropagation networks
Neural Computation
Bayesian regularization and pruning using a Laplace prior
Neural Computation
Regression with input-dependent noise: a Gaussian process treatment
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Modelling seasonality and trends in daily rainfall data
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Bayesian Inference of Noise Levels in Regression
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Using neural networks to model conditional multivariate densities
Neural Computation
Neural Networks
Quantile regression model for impact toughness estimation
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Exceedance probability estimation for a quality test consisting of multiple measurements
Expert Systems with Applications: An International Journal
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Artificial neural networks have proved an attractive approach to non-linear regression problems arising in environmental modelling, such as statistical downscaling, short-term forecasting of atmospheric pollutant concentrations and rainfall run-off modelling. However, environmental datasets are frequently very noisy and characterized by a noise process that may be heteroscedastic (having input dependent variance) and/or non-Gaussian. The aim of this paper is to review existing methodologies for estimating predictive uncertainty in such situations and, more importantly, to illustrate how a model of the predictive distribution may be exploited in assessing the possible impacts of climate change and to improve current decision making processes. The results of the WCCI-2006 predictive uncertainty in environmental modelling challenge are also reviewed, suggesting a number of areas where further research may provide significant benefits.