Results for stat.ME

Publications & Outputs

1. Scalable Monte Carlo for Bayesian Learning

   Fearnhead, P., Nemeth, C., Oates, C. J. & Sherlock, C., 5/05/2025, Cambridge: Cambridge University Press. ( Institute of Mathematical Statistics Monographs)

   Research output: Book/Report/Proceedings › Book

2. Scalable calibration of individual-based epidemic models through categorical approximations

   Rimella, L., Whiteley, N., Jewell, C., Fearnhead, P. & Whitehouse, M., 7/01/2025, Arxiv.

   Research output: Working paper › Preprint

3. PDMP Monte Carlo methods for piecewise-smooth densities

   Chevallier, A., Power, S., Wang, A. Q. & Fearnhead, P., 7/11/2024, In: Advances in Applied Probability. 56, 4, p. 1153-1194 42 p.

   Research output: Contribution to Journal/Magazine › Journal article

4. Treatment-control comparisons in platform trials including non-concurrent controls

   Roig, M. B., Krotka, P., Hees, K., Koenig, F., Magirr, D., Jacko, P., Parke, T. & Posch, M., 18/07/2024.

   Research output: Working paper › Preprint

5. Exact statistical analysis for response-adaptive clinical trials: a general and computationally tractable approach

   Baas, S., Jacko, P. & Villar, S. S., 1/07/2024, Arxiv.

   Research output: Working paper › Preprint

6. Semi-Supervised Learning guided by the Generalized Bayes Rule under Soft Revision

   Dietrich, S., Rodemann, J. & Jansen, C., 24/05/2024, Arxiv.

   Research output: Working paper › Preprint

7. Approximating optimal SMC proposal distributions in individual-based epidemic models

   Rimella, L., Jewell, C. & Fearnhead, P., 31/03/2024, In: Statistica Sinica. 34, Online Special Issue 1, 38 p., 6.

   Research output: Contribution to Journal/Magazine › Journal article › peer-review

8. Concave-Convex PDMP-based sampling

   Sutton, M. & Fearnhead, P., 2/10/2023, In: Journal of Computational and Graphical Statistics. 32, 4, 22 p.

   Research output: Contribution to Journal/Magazine › Journal article › peer-review

9. CoinEM: Tuning-Free Particle-Based Variational Inference for Latent Variable Models

   Sharrock, L., Dodd, D. & Nemeth, C., 24/05/2023.

   Research output: Working paper › Preprint

10. Learning Rate Free Bayesian Inference in Constrained Domains

    Sharrock, L., Mackey, L. & Nemeth, C., 24/05/2023.

    Research output: Working paper › Preprint

11. Automatic Locally Stationary Time Series Forecasting with application to predicting U.K. Gross Value Added Time Series under sudden shocks caused by the COVID pandemic

    Killick, R., Knight, M. I., Nason, G. P., Nunes, M. A. & Eckley, I. A., 14/03/2023.

    Research output: Working paper › Preprint

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