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stat.ME2025
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…
stat.ME2025
AI-Powered Bayesian Inference
Sean O'Hagan, Veronika Ročková
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…
stat.ME2024
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…