The article discusses the importance of heteroscedastic predictive uncertainties in Bayesian Neural Networks (BNNs). The authors illustrate how both heteroscedastic aleatoric and epistemic variance can be integrated into the variances…
Continue readingThe paper delves into the concept of realisability in statistical learning theory under the assumption of epistemic uncertainty. The author considers a scenario where the train and test distribution are…
Continue readingThe paper discusses an approach to model epistemic uncertainty in Conjunction Data Messages (CDMs) and classify conjunction events based on the confidence in the collision probability. The proposed approach uses…
Continue readingThe paper ‘Bayesian Semi-structured Subspace Inference’ presents a new method to address the problem of epistemic uncertainty in semi-structured regression models. The researchers propose a Bayesian approximation using subspace inference…
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