5 papers
Supercharging Bayesian Inference with Reliable AI-Informed Priors
Jongwoo Choi, Sean O'Hagan
Modern predictive systems encode beliefs that can act as useful prior information for statistical inference in data-limited settings. Using them for prior construction introduces a…
Generative Bayesian Filtering and Parameter Learning
Edoardo Marcelli, Sean O'Hagan, Veronika Rockova
Generative Bayesian Filtering (GBF) provides a powerful and flexible framework for performing posterior inference in complex nonlinear and non-Gaussian state-space models. Our appr…
AI-Powered Bayesian Inference
Sean O'Hagan, Veronika RoÄková
The advent of Generative Artificial Intelligence (GAI) has heralded an inflection point that changed how society thinks about knowledge acquisition. While GAI cannot be fully trust…
Adaptive Uncertainty Quantification for Generative AI
Jungeum Kim, Sean O'Hagan, Veronika Rockova
This work is concerned with conformal prediction in contemporary applications (including generative AI) where a black-box model has been trained on data that are not accessible to…
Tree Bandits for Generative Bayes
Sean O'Hagan, Jungeum Kim, Veronika Rockova
In generative models with obscured likelihood, Approximate Bayesian Computation (ABC) is often the tool of last resort for inference. However, ABC demands many prior parameter tria…