A bias-reduced Bayesian optimal experimental design procedure using Kullback-Leibler divergence is shown to select high-value steel mass-flow observations that reduce network-structure uncertainty in a U.S. steel MFA, with the optimum depending on total data budget.
A sequential monte carlo approach to the sequential design for discriminating between rival continuous data models, 2012
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Intelligent data collection for network discrimination in material flow analysis using Bayesian optimal experimental design
A bias-reduced Bayesian optimal experimental design procedure using Kullback-Leibler divergence is shown to select high-value steel mass-flow observations that reduce network-structure uncertainty in a U.S. steel MFA, with the optimum depending on total data budget.