Invited Talk
Surrogate-Based Methods for Efficient Bayesian Posterior Computation
Dr. Chengkun Li · Invited Speaker
Abstract
Bayesian inference provides a principled framework for quantifying uncertainty in parameter estimation and model selection. Applying Bayesian methods to complex forward models remains computationally demanding, particularly when likelihood evaluations are expensive or noisy, or when inference must be repeated across many observed datasets.
This talk presents two classes of surrogate-based methods developed to address these challenges, including surrogates trained via regression and surrogates trained via conditional density estimation.
Speaker Bio
Dr. Chengkun Li is an invited speaker at AIEPS 2026.
Materials and Images
- Slides: To be added.
- Related links: To be added.
- Images: To be added.