Optimizing Likelihoods via Mutual Information: Bridging Simulation-Based Inference and Bayesian Optimal Experimental Design
The episode discusses a paper that unifies Simulation-Based Inference (SBI) and Bayesian Optimal Experimental Design (BOED). The authors propose a method using mutual information to simultaneously train likelihood models and select optimal experiments, making it effective even for complex, non-differentiable simulators.