Bayesian inference with sources of uncertainty: from confidence modelling to sparse estimation
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Abstract (arXiv)
We introduce a general framework that extends Bayesian inference by allowing the researcher to explicitly encode confidence in each source of uncertainty within the model. This mechanism provides a new handle for model design and regularisation control. Building on this framework, we develop a general approach for inducing sparsity in statistical models and illustrate its use in linear and logistic regression, as well as in Bayesian neural networks.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 31 references that are in the map. First posted on arXiv 4 May 2026.
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8 of 31 references.
References · 8
- 2013Black Box Variational Inferencecited by 319
- 2013A General Framework for Updating Belief Distributionscited by 175
- 2021The Bayesian Learning Rulecited by 99
- 2014Inconsistency of Bayesian Inference for Misspecified Linear Models, and a Proposal for Repairing Itcited by 95
- 2017Model Selection in Bayesian Neural Networks via Horseshoe Priorscited by 52
- 2017Assigning a value to a power likelihood in a general Bayesian modelcited by 46
- 2018Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priorscited by 35
- 2015Robust Bayesian inference via coarseningcited by 32
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