6 papers
Simulation-Free Learning of GP-SDEs from Irregular Observations
Zhidi Lin, Yuhao Liu, Ying Li +2
Gaussian process stochastic differential equations (GP-SDEs) provide a flexible Bayesian model for unknown continuous-time state dynamics with uncertainty quantification, but learn…
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…
On the design distribution for predictive Bayesian regression
Wanyue Sun, Edwin Fong
The predictive approach to Bayesian inference accesses the posterior distribution via a sequence of one-step-ahead predictives, enabling inference via predictive resampling without…
Instrumental and Proximal Causal Inference with Gaussian Processes
Yuqi Zhang, Krikamol Muandet, Dino Sejdinovic +2
Instrumental variable (IV) and proximal causal learning (Proxy) methods are central frameworks for causal inference in the presence of unobserved confounding. Despite substantial m…
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…
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…