7 papers · 1 filter
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…