Reliable uncertainty estimates in deep learning with efficient Metropolis-Hastings algorithms

Matthias Schmal, Patrick Mäder

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DOI: 10.1038/s41467-026-70015-z
Published: 2026-03-17
Container: Nature Communications
Publisher: Springer Science and Business Media LLC
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1038/s41467-026-70015-z,
  title = {Reliable uncertainty estimates in deep learning with efficient Metropolis-Hastings algorithms},
  author = {Matthias Schmal and Patrick Mäder},
  year = {2026},
  journal = {Nature Communications},
  doi = {10.1038/s41467-026-70015-z},
  url = {https://doi.org/10.1038/s41467-026-70015-z}
}

RIS

TY  - JOUR
TI  - Reliable uncertainty estimates in deep learning with efficient Metropolis-Hastings algorithms
AU  - Matthias Schmal
AU  - Patrick Mäder
PY  - 2026
JO  - Nature Communications
DO  - 10.1038/s41467-026-70015-z
UR  - https://doi.org/10.1038/s41467-026-70015-z
ER  -

APA

Schmal, M., & Mäder, P. (2026). Reliable uncertainty estimates in deep learning with efficient Metropolis-Hastings algorithms. Nature Communications. https://doi.org/10.1038/s41467-026-70015-z

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crossref · retrieved 2026-11-10T18:15:52.197Z