Gradient-based stochastic optimization methods in Bayesian experimental design

Understand

Optimal experimental design (OED) seeks experiments expected to yield the most useful data for some purpose.

• In practical circumstances where experiments are time-consuming or resource-intensive, OED can yield enormous savings.
• We pursue OED for nonlinear systems from a Bayesian perspective, with the goal of choosing experiments that are optimal for parameter inference.
• Our objective in this context is the expected information gain in model parameters, which in general can only be estimated using Monte Carlo methods.