Dr. Jack Jewson
I am currently working as a Postdoctoral Assistant at the Barcelona School of Economics (BSE), Universitat Pompeu Fabra (UPF), working with Dr. David Rossell and Dr. Piotr Zweirnik.
Form 1st March 2022 I will start a 2 year Juan de la Cierva Postdoctoral Fellowship.
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 2019, Awarded 2020).
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.
Model misspecification, robust statistics and the M-open world
Divergences, loss functions and scoring rules
Articles and Publications
Bias Mitigated Learning from Differentially Private Synthetic Data: A Cautionary Tale: Ghalebikesabi, S., Wilde, H., Jewson, J., Doucet, A., Vollmer, S., Holmes, C., arXiv preprint arXiv:2108.10934 (2021)
General Bayesian Loss Function Selection and the use of Improper Models: Jewson, J., Rossell, D., arXiv preprint arXiv:2106.01214 (2021)
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).
Bayesian Approximations to Hidden Semi-Markov Models: Hadj-Amar, B., Jewson, J., Fiecas, M., arXiv preprint arXiv:2006.09061 (2020)
Generalized Variational Inference: Knoblauch, J., Jewson, J., Damoulas, T., arXiv preprint arXiv:1904.02063 (2019).
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
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).
My PhD thesis titled ``Bayesian Inference in the M-open world'' is available here
My MMorse masters thesis examining the Duckworth-Lewis method in the context of English county cricket is here