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Peer-reviewedCC BY 4.0ESAIM: Proceedings and Surveys

Modern Bayesian Modeling and Inference

  • Julyan Arbel
  • Paul Egels
  • Stefano Favaro
  • Kaniav Kamary
  • Zacharie Naulet
  • Pierre Wolinski
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Abstract

This article presents a summary of the contributions to the Bayesian statistics session held at the Journées MAS 2024. The session highlighted recent advances across several active areas of Bayesian methodology, including model selection in probabilistic principal component analysis, heavy-tailed priors in Bayesian deep learning, the interplay between prior specification and penalization in variational inference, and Bayesian nonparametric approaches to species sampling problems. Each contribution illustrates both theoretical insights and methodological developments, reflecting the richness and diversity of current research in the Bayesian community.

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Cite

BibTeXRIS
BibTeX
@article{arbel2026modern,
  title = {{Modern Bayesian Modeling and Inference}},
  author = {Julyan Arbel and Paul Egels and Stefano Favaro and Kaniav Kamary and Zacharie Naulet and Pierre Wolinski},
  journal = {ESAIM: Proceedings and Surveys},
  year = {2026},
  month = sep,
  url = {https://inria.hal.science/hal-05746900v1},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Modern Bayesian Modeling and Inference
AU  - Julyan Arbel
AU  - Paul Egels
AU  - Stefano Favaro
AU  - Kaniav Kamary
AU  - Zacharie Naulet
AU  - Pierre Wolinski
PY  - 2026
DA  - 2026/09/11
JO  - ESAIM: Proceedings and Surveys
UR  - https://inria.hal.science/hal-05746900v1
AB  - This article presents a summary of the contributions to the Bayesian statistics session held at the Journées MAS 2024. The session highlighted recent advances across several active areas of Bayesian methodology, including model selection in probabilistic principal component analysis, heavy-tailed priors in Bayesian deep learning, the interplay between prior specification and penalization in variational inference, and Bayesian nonparametric approaches to species sampling problems. Each contribution illustrates both theoretical insights and methodological developments, reflecting the richness and diversity of current research in the Bayesian community.
SN  - 2267-3059
ER  -