77 citations · 154 across the 17 of their papers we have counts for
19 papers
Bayesian inference under model misspecification
Jack Jewson
The likelihood input to a Bayesian analysis almost never exactly represents how the data were generated, calling into question the validity of posterior inferences. We review a var…
Fair Variable Selection
Deborah Sulem, Jack Jewson
Algorithms are increasingly being used to help automate and improve data-driven decisions, but care must be taken to prevent such algorithms from learning discriminatory patterns f…
Variational predictive resampling
Laura Battaglia, Stefano Cortinovis, Chris Holmes +2
Bayesian inference provides principled uncertainty quantification, but accurate posterior sampling with MCMC can be computationally prohibitive for modern applications. Variational…
Bayesian Variable Selection with the Quasi-Posterior
Beniamino Hadj-Amar, Jack Jewson
The Bayesian approach provides powerful methods for variable selection. The ability to incorporate sparsity through prior beliefs and account for parameter uncertainty allows Bayes…
Bayesian computation for high-dimensional Gaussian Graphical Models with spike-and-slab priors
Deborah Sulem, Jack Jewson, David Rossell
Gaussian graphical models are widely used to infer dependence structures. Bayesian methods are appealing to quantify uncertainty associated with structural learning, i.e., the plau…
Probabilistic Programming with Sufficient Statistics for faster Bayesian Computation
Clemens Pichler, Jack Jewson, Alejandra Avalos-Pacheco
Probabilistic programming methods have revolutionised Bayesian inference, making it easier than ever for practitioners to perform Markov-chain-Monte-Carlo sampling from non-conjuga…
Exact Sampling of Gibbs Measures with Estimated Losses
David T. Frazier, Jeremias Knoblauch, Jack Jewson +1
A popular strategy for ameliorating some of the shortcomings of Bayesian posterior inference is to instead target a Gibbs measure based on losses that connect a parameter of intere…
Differentially Private Statistical Inference through -Divergence One Posterior Sampling
Jack Jewson, Sahra Ghalebikesabi, Chris Holmes
Differential privacy guarantees allow the results of a statistical analysis involving sensitive data to be released without compromising the privacy of any individual taking part.…
Bayesian Sparse Vector Autoregressive Switching Models with Application to Human Gesture Phase Segmentation
Beniamino Hadj-Amar, Jack Jewson, Marina Vannucci
We propose a sparse vector autoregressive (VAR) hidden semi-Markov model (HSMM) for modeling temporal and contemporaneous (e.g. spatial) dependencies in multivariate nonstationary…
On the Stability of General Bayesian Inference
Jack Jewson, Jim Q. Smith, Chris Holmes
We study the stability of posterior predictive inferences to the specification of the likelihood model and perturbations of the data generating process. In modern big data analyses…
Graphical model inference with external network data
Jack Jewson, Li Li, Laura Battaglia +3
We consider two applications where we study how dependence structure between many variables is linked to external network data. We first study the interplay between social media co…
Mitigating Statistical Bias within Differentially Private Synthetic Data
Sahra Ghalebikesabi, Harrison Wilde, Jack Jewson +3
Increasing interest in privacy-preserving machine learning has led to new and evolved approaches for generating private synthetic data from undisclosed real data. However, mechanis…
General Bayesian Loss Function Selection and the use of Improper Models
Jack Jewson, David Rossell
Statisticians often face the choice between using probability models or a paradigm defined by minimising a loss function. Both approaches are useful and, if the loss can be re-cast…
Foundations of Bayesian Learning from Synthetic Data
Harrison Wilde, Jack Jewson, Sebastian Vollmer +1
There is significant growth and interest in the use of synthetic data as an enabler for machine learning in environments where the release of real data is restricted due to privacy…
Bayesian Approximations to Hidden Semi-Markov Models for Telemetric Monitoring of Physical Activity
Beniamino Hadj-Amar, Jack Jewson, Mark Fiecas
We propose a Bayesian hidden Markov model for analyzing time series and sequential data where a special structure of the transition probability matrix is embedded to model explicit…
Generalized Variational Inference: Three arguments for deriving new Posteriors
Jeremias Knoblauch, Jack Jewson, Theodoros Damoulas
We advocate an optimization-centric view on and introduce a novel generalization of Bayesian inference. Our inspiration is the representation of Bayes' rule as infinite-dimensional…
Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with -Divergences
Jeremias Knoblauch, Jack Jewson, Theodoros Damoulas
We present the very first robust Bayesian Online Changepoint Detection algorithm through General Bayesian Inference (GBI) with -divergences. The resulting inference procedure is…
Principles of Bayesian Inference using General Divergence Criteria
Jack Jewson, Jim Q Smith, Chris Holmes
When it is acknowledged that all candidate parameterised statistical models are misspecified relative to the data generating process, the decision maker (DM) must currently concern…
Some discussions on the Read Paper "Beyond subjective and objective in statistics" by A. Gelman and C. Hennig
Gilles Celeux, Jack Jewson, Julie Josse +2
This note is a collection of several discussions of the paper "Beyond subjective and objective in statistics", read by A. Gelman and C. Hennig to the Royal Statistical Society on A…