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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Compositional periodic splines in Bayes spaces enable approximation of circular density data via centered log-ratio transformation and matrix-based penalized estimation, shown on wind direction records.
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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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Compositional Periodic Spline Approximation for Circular Density Data in Bayes Spaces
Compositional periodic splines in Bayes spaces enable approximation of circular density data via centered log-ratio transformation and matrix-based penalized estimation, shown on wind direction records.