Current Position
I am currently a Senior Lecturer in the Department of Econometrics and Business Statistics at Monash University in Australia.
Previous Positions
Mar 2022 – February 2024: Juan de la Cierva Research Fellow, Department of Economics and Business, Universitat Pompeu Fabra (UPF), working with Dr. David Rossell.
Sept 2019 – Mar 2022: Postdoctoral Research Assistant, Barcelona School of Economics (BSE), Universitat Pompeu Fabra (UPF), working with Dr. David Rossell and Dr. Piotr Zweirnik.
Education
I obtained my PhD titled “Bayesian Inference in the M-open world” from the University of Warwick (in collaboration with the University of Oxford) as part of the Oxford-Warwick Statistics Programme (OxWaSP) under the supervision of Prof. Jim Q. Smith (Warwick) and Prof. Chris Holmes (Oxford) (Submitted Sept 2019, Awarded June 2020).
Research Interests
My research interests surround the methodological and philosophical challenges encountered when conducting Bayesian analyses for modern, high dimensional inference problems. In such scenarios a ‘full’ Bayesian analysis is inevitably impossible and one must resort to approximations, either at a modelling or a computational level.
I am particularly interested in general Bayesian updating and the use of loss functions to make principled Bayesian inference and improved decision making.
Other interests:
- Model misspecification, robust statistics and the M-open world
- Divergences, loss functions and scoring rules
- Variable selection and graphical modelling
- Differential privacy
ArXiv Preprints
Variational predictive resampling: Battaglia L., Cortinovis S., Holmes C., Frazier D.T., Jewson J., 2026. arXiv preprint arXiv:2605.11168.
Bayesian Variable Selection with the Quasi-Posterior: Hadj-Amar, B. and Jewson, J., 2026. arXiv preprint arXiv:2601.12767.
Bayesian computation for high-dimensional Gaussian Graphical Models with spike-and-slab priors: Sulem, D., Jewson, J., and Rossell, D., 2025. arXiv preprint arXiv:2511.01875. (A link to Deborah’s WiNS Seminar Feb 2025.)
Exact Sampling of Gibbs Measures with Estimated Losses: Frazier, D.T., Knoblauch, J., Jewson, J., and Drovandi, C., 2025. arXiv preprint arXiv:2404.15649.
Probabilistic Programming with Sufficient Statistics for faster Bayesian Computation: Pichler, C., Jewson, J. and Avalos-Pacheco, A., 2025. arXiv preprint arXiv:2502.04990.
Publications
On the Stability of General Bayesian Inference: Jewson, J., Smith, J. Q., Holmes, C. Bayesian Analysis, 1 (1), 1–31. 2024.
Graphical model inference with external network data: Jewson, J., Li, L., Battaglia, L., Hansen, S., Rossell, D., Zwiernik, P. Biometrics, 80 (4), ujae151. 2024.
Bayesian Sparse Vector Autoregressive Switching Models with Application to Human Gesture Phase Segmentation: Hadj-Amar, B., Jewson, J., Vannucci, M. Annals of Applied Statistics, 18.3, 2511–2531. 2024.
Differentially Private Statistical Inference through β-Divergence One Posterior Sampling: Jewson, J., Ghalebikesabi, S., Holmes, C. Advances in Neural Information Processing Systems (NeurIPS). 2023.
Bayesian Approximations to Hidden Semi-Markov Models for Telemetric Monitoring of Physical Activity: Hadj-Amar, B., Jewson, J., Fiecas, M. Bayesian Analysis, 18 (2), 547–577. 2023.
General Bayesian Loss Function Selection and the use of Improper Models: Jewson, J., Rossell, D. Journal of the Royal Statistical Society: Series B, 84 (5), 1640–1665. 2022.
An Optimization-centric View on Bayes’ Rule: Reviewing and Generalizing Variational Inference: Knoblauch, J., Jewson, J., Damoulas, T. Journal of Machine Learning Research, 23 (132), 1–109. 2022.
Mitigating statistical bias within differentially private synthetic data: Ghalebikesabi, S., Wilde, H., Jewson, J., Doucet, A., Vollmer, S., Holmes, C. In The 38th Conference on Uncertainty in Artificial Intelligence (UAI 2022).
Foundation of Bayesian Learning from Synthetic Data: Wilde, H., Jewson, J., Vollmer, S., Holmes, C. In International Conference on Artificial Intelligence and Statistics (AISTATS 2021), pp. 541–549. PMLR.
Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with β-Divergences: Knoblauch, J., Jewson, J., Damoulas, T. Advances in Neural Information Processing Systems (NeurIPS). 2018. (poster) (video)
Principles of Bayesian Inference Using General Divergence Criteria: Jewson, J., Smith, J.Q., Holmes, C. Entropy, 20, 442. 2018.
A comment on the Duckworth–Lewis–Stern method: Comments on “The Duckworth-Lewis-Stern method: extending the Duckworth-Lewis methodology to deal with modern scoring rates” by S.E. Stern, appearing as a viewpoint in JORS (2017). S.E. Stern’s response can be found here.
Discussions
Learning Summary Statistic Hyperparameters: Discussion (p60) on the Read Paper “Bayesian Restricted Likelihood Methods: Conditioning on Insufficient Statistics in Bayesian Regression” by J. Lewis, S. MacEachern, and Y. Lee, appearing in Bayesian Analysis (2021).
Subjective Bayesian Updating: My discussion on the Read Paper “Beyond subjective and objective in statistics” by A. Gelman and C. Hennig; a condensed version appears in JRSSA (2017).
