Action-BED recasts BED as expected future loss on actions, producing singly intractable objectives jointly optimized for design and action policies via stochastic gradients without explicit posterior estimation.
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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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Action-BED: Task-Driven Bayesian Experimental Design with Singly Intractable Objectives
Action-BED recasts BED as expected future loss on actions, producing singly intractable objectives jointly optimized for design and action policies via stochastic gradients without explicit posterior estimation.
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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.