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The two objectives when estimating the conditional density function in a regression problem are to maximise sharpness (the density rewarded to the actual observation), while maintaining calibration (the empirical validity of the probability estimates). In this paper we outline a process of optimisation that maximises both these objectives simultaneously to make better probabilistic predictions.
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