A layered multiple importance sampling scheme for focused optimal Bayesian experimental design
read the original abstract
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.
This paper has not been read by Pith yet.
Forward citations
Cited by 2 Pith papers
-
Mean--Variance Risk-Aware Bayesian Optimal Experimental Design for Nonlinear Models
A variance-penalized Bayesian optimal experimental design method for nonlinear models uses prior-sampling Monte Carlo estimators and Bayesian optimization to identify robust designs with reduced utility variability.
-
Bayesian experimental design: grouped geometric pooled posterior via ensemble Kalman methods
A grouped pooling strategy with ensemble Kalman inversion improves accuracy of expected information gain estimators in Bayesian experimental design at amortized computational cost.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.