vsOED uses a variational one-point reward and RL policy optimization to provide a lower bound on expected information gain for sequential experimental design, supporting nuisance parameters, implicit likelihoods, and multiple design goals.
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A grouped pooling strategy with ensemble Kalman inversion improves accuracy of expected information gain estimators in Bayesian experimental design at amortized computational cost.
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Variational Sequential Optimal Experimental Design using Reinforcement Learning
vsOED uses a variational one-point reward and RL policy optimization to provide a lower bound on expected information gain for sequential experimental design, supporting nuisance parameters, implicit likelihoods, and multiple design goals.
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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.