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.
Journal of Business & Economic Statistics , volume =
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Matrix-HAR model with multi-horizon lags and renewable generation inputs improves one-week forecasts of realized covariation and spread risk premia versus standard backward-looking volatility methods in electricity markets.
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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.