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Jack.Jewson@monash.edu
Current Position:
Current Position:
I am currently a Senior Lecturer in the Department of Econometrics and Business Statistics at Monash University in Australia
Previous Positions:
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:
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:
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
ArXiv Preprints
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
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 (Statistical Methodology), 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, 2022 (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 (pp. 541-549). PMLR, 2021 (AISTATS, 2021).
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 2018, 20, 442
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).
Other
My PhD Thesis titled ''Bayesian Inference in the M-open world'' is available here
My MMorse Master's Thesis examining the Duckworth-Lewis method in the context of English county cricket is here
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