77 citations · 154 across the 17 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…
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…
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…