An efficient Bayesian data augmentation algorithm for continuous-time stochastic epidemic models fit to partially observed incidence data

Haoyu Wu, David A Stephens, Erica E M Moodie

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DOI: 10.1093/biomtc/ujag187
Published: 2026-10-07
Container: Biometrics
Publisher: Oxford University Press (OUP)
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1093/biomtc/ujag187,
  title = {An efficient Bayesian data augmentation algorithm for continuous-time stochastic epidemic models fit to partially observed incidence data},
  author = {Haoyu Wu and David A Stephens and Erica E M Moodie},
  year = {2026},
  journal = {Biometrics},
  doi = {10.1093/biomtc/ujag187},
  url = {https://doi.org/10.1093/biomtc/ujag187}
}

RIS

TY  - JOUR
TI  - An efficient Bayesian data augmentation algorithm for continuous-time stochastic epidemic models fit to partially observed incidence data
AU  - Haoyu Wu
AU  - David A Stephens
AU  - Erica E M Moodie
PY  - 2026
JO  - Biometrics
DO  - 10.1093/biomtc/ujag187
UR  - https://doi.org/10.1093/biomtc/ujag187
ER  -

APA

Wu, H., Stephens, D. A., & Moodie, E. E. M. (2026). An efficient Bayesian data augmentation algorithm for continuous-time stochastic epidemic models fit to partially observed incidence data. Biometrics. https://doi.org/10.1093/biomtc/ujag187

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crossref · retrieved 2026-11-07T05:04:27.486Z