18 citations · 27 across the 6 of their papers we have counts for
7 papers
Inverse Probability Weighting in a Post-Bayesian World
Owen Thomas, William Denault, Valeria Vitelli
We present a justification of the use of Inverse Probability Weighting (IPW) in a post-Bayesian framework, in which the bias-correction provided by IPW in a frequentist context is…
Prior knowledge elicitation: The past, present, and future
Petrus Mikkola, Osvaldo A. Martin, Suyog Chandramouli +9
Specification of the prior distribution for a Bayesian model is a central part of the Bayesian workflow for data analysis, but it is often difficult even for statistical experts. I…
Generalised Bayes Updates with -divergences through Probabilistic Classifiers
Owen Thomas, Henri Pesonen, Jukka Corander
A stream of algorithmic advances has steadily increased the popularity of the Bayesian approach as an inference paradigm, both from the theoretical and applied perspective. Even wi…
Probabilistic elicitation of expert knowledge through assessment of computer simulations
Owen Thomas, Henri Pesonen, Jukka Corander
We present a new method for probabilistic elicitation of expert knowledge using binary responses of human experts assessing simulated data from a statistical model, where the param…
Misspecification-robust likelihood-free inference in high dimensions
Owen Thomas, Raquel Sá-Leão, Hermínia de Lencastre +3
Likelihood-free inference for simulator-based statistical models has developed rapidly from its infancy to a useful tool for practitioners. However, models with more than a handful…
Diagnosing model misspecification and performing generalized Bayes' updates via probabilistic classifiers
Owen Thomas, Jukka Corander
Model misspecification is a long-standing enigma of the Bayesian inference framework as posteriors tend to get overly concentrated on ill-informed parameter values towards the larg…