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A simple method for adding uncertainty to neural network regression tasks viaestimation of a general probability distribution is described. The methodologysupports estimation of heteroscedastic, asymmetric uncertainties by a simplemodification of the network output and loss function. Method performance isdemonstrated with a simple one dimensional data set and then applied to a morecomplex regression task using synthetic climate data.

Adding Uncertainty to Neural Network Regression Tasks in the Geosciences