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PreprintCC BY 4.0HAL

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

  • Rosa Rafael Mouallem
  • Julyan Arbel
  • Nguyen Hien Duy
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Abstract

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.

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Cite

BibTeXRIS
BibTeX
@misc{mouallem2026empowering,
  title = {{Empowering Bayes with sources of uncertainty: from confidence modelling to sparse estimation}},
  author = {Rosa Rafael Mouallem and Julyan Arbel and Nguyen Hien Duy},
  howpublished = {HAL},
  year = {2026},
  month = oct,
  url = {https://hal.science/hal-05746924v2},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - Empowering Bayes with sources of uncertainty: from confidence modelling to sparse estimation
AU  - Rosa Rafael Mouallem
AU  - Julyan Arbel
AU  - Nguyen Hien Duy
PY  - 2026
DA  - 2026/10/21
PB  - HAL
UR  - https://hal.science/hal-05746924v2
AB  - 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.
ER  -