Tag: Statistical Methods and Bayesian Inference
Derivative-free Bayesian design method for sequential settings
What the study found The study introduces a gradient-free framework for sequential Bayesian optimal experimental design, a way of choosing experiments using probability and uncertainty, for complex systems where gradient information is unavailable. The framework combines Ensemble Kalman Inversion and the Affine-Invariant Langevin Dynamics sampler, and uses variational Gaussian and parametrized Laplace approximations to make…
