2 citations · 4 across the 12 of their papers we have counts for
5 papers · 1 filter
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 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…
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…
Deep Bayes Factors
Jungeum Kim, Veronika Rockova
The is no other model or hypothesis verification tool in Bayesian statistics that is as widely used as the Bayes factor. We focus on generative models that are likelihood-free and,…
Generative Bayesian Inference with GANs
Yuexi Wang, Veronika Ročková
In the absence of explicit or tractable likelihoods, Bayesians often resort to approximate Bayesian computation (ABC) for inference. Our work bridges ABC with deep neural implicit…