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arXiv Statistics ML By Rafael Mouallem Rosa, Julyan Arbel, Hien Duy Nguyen

Empowering Bayes with sources of uncertainty: from confidence modelling to sparse estimation

Read the original on arXiv Statistics ML →

The paper introduces Sources of Uncertainty (SoU) inference, a framework that extends Bayesian inference by allowing heterogeneous confidence to be encoded across model components. This selective treatment controls the uncertainty retained in each component and its propagation, yielding Empirical Bayes and profiling-type inference as special cases. The authors also present a general method for inducing sparsity, demonstrating its application in linear and logistic regression and in Bayesian neural networks for self‑pruning.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Statistics ML.

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