Bayesian inference with sources of uncertainty: from confidence modelling to sparse estimation

Fuente: arXiv
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Main Authors: Rosa, Rafael Mouallem, Arbel, Julyan, Nguyen, Hien Duy
Format: Preprint
Published: 2026
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author Rosa, Rafael Mouallem
Arbel, Julyan
Nguyen, Hien Duy
author_facet Rosa, Rafael Mouallem
Arbel, Julyan
Nguyen, Hien Duy
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2605_03134
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Bayesian inference with sources of uncertainty: from confidence modelling to sparse estimation
Rosa, Rafael Mouallem
Arbel, Julyan
Nguyen, Hien Duy
Methodology
Machine Learning
62F15, 62C10, 62J07, 62J12
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.
title Bayesian inference with sources of uncertainty: from confidence modelling to sparse estimation
topic Methodology
Machine Learning
62F15, 62C10, 62J07, 62J12
url https://arxiv.org/abs/2605.03134