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Found 3 papers, 2 papers with code
Correcting Predictions for Approximate Bayesian Inference
Bayesian models quantify uncertainty and facilitate optimal decision-making in downstream applications.
Variational Bayesian Decision-making for Continuous Utilities
Bayesian decision theory outlines a rigorous framework for making optimal decisions based on maximizing expected utility over a model posterior.
Importance Sampled Stochastic Optimization for Variational Inference
Variational inference approximates the posterior distribution of a probabilistic model with a parameterized density by maximizing a lower bound for the model evidence.
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