Fast, Optimal, and Targeted Predictions using Parametrized Decision Analysis.

ABSTRACT: Prediction is critical for decision-making under uncertainty and lends validity to statistical inference. With targeted prediction, the goal is to optimize predictions for specific decision tasks of interest, which we represent via functionals. Although classical decision analysis extracts predictions from a Bayesian model, these predictions are often difficult to interpret and slow to compute. Instead, we design a class of parametrized actions for Bayesian decision analysis that produce optimal, scalable, and simple targeted predictions. For a wide variety of action parametrizations and loss functions-including linear actions with sparsity constraints for targeted variable selection-we derive a convenient representation of the optimal targeted prediction that yields efficien

SUBMITTER: Kowal DR 

PROVIDER: S-EPMC9970289 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature