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.
Moˇ ckus.On Bayesian methods for seeking the extremum, page 400–404
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ME 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.