A layered multiple importance sampling scheme for focused optimal Bayesian experimental design

Understand

We develop a new computational approach for "focused" optimal Bayesian experimental design with nonlinear models, with the goal of maximizing expected information gain in targeted subsets of model parameters.

• Our approach considers uncertainty in the full set of model parameters, but employs a design objective that can exploit learning trade-offs among different parameter subsets.
• We introduce a new layered multiple importance sampling scheme that provides consistent estimates of expected information gain in this focused setting.
• This sampling scheme yields significant reductions in estimator bias and variance for a given computational effort, making optimal design more tractable for a wide range of computationally intensive problems.