8 papers
Markov chain Monte Carlo for predictively oriented posteriors
Yann McLatchie, Louis Sharrock, David T. Frazier +1
The predictively oriented posterior offers principled uncertainty quantification, even under model misspecification. However, it does not admit an explicit density and therefore ca…
Bagged Martingale Posteriors: Calibrated Uncertainty Quantification for Predictive Resampling
Hui Wang, Edwin Fong, David T. Frazier
Martingale posteriors and related predictive resampling methods replace the likelihood--prior pair used within Bayesian inference with a predictive model for future observations. T…
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…
Anomaly detection using surprisals
Rob J Hyndman, David T. Frazier
Anomaly detection methods are widely used but often rely on ad hoc rules or strong assumptions, and they often focus on tail events, missing ``inlier'' anomalies that occur in low-…
Bayesian probabilistic projections of proportions with limited data: An application to subnational contraceptive method supply shares
Hannah Comiskey, Niamh Cahill, Leontine Alkema +2
Engaging the private sector in contraceptive method supply is critical for creating equitable, sustainable, and accessible healthcare systems. To achieve this, it is essential to u…
TabMGP: Martingale Posterior with TabPFN
Kenyon Ng, Edwin Fong, David T. Frazier +2
Bayesian inference provides principled uncertainty quantification but is often limited by the challenges of prior and likelihood elicitation. The martingale posterior (MGP) (Fong e…
Predictively Oriented Posteriors
Yann McLatchie, Badr-Eddine Cherief-Abdellatif, David T. Frazier +1
We advocate for a new statistical principle that combines the most desirable aspects of both parameter inference and density estimation. This leads us to the predictively oriented…
Predictive performance of power posteriors
Yann McLatchie, Edwin Fong, David T. Frazier +1
We analyse the impact of using tempered likelihoods in the production of posterior predictions. While the choice of temperature has an impact on predictive performance in small sam…