Generative Bayesian Computation for Maximum Expected Utility

Generative Bayesian Computation for Maximum Expected Utility

Generative Bayesian Computation (GBC) methods are developed to provide an efficient computational solution for maximum expected utility (MEU). We propose a density-free generative method based on quantiles that naturally calculates expected utility as a marginal of posterior quantiles. Our approach...

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Bibliographic Details
Main Authors: Nick Polson, Fabrizio Ruggeri, Vadim Sokolov
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Entropy
Subjects:
generative methods
quantile networks
decision theory
Bayesian computations
Online Access:https://www.mdpi.com/1099-4300/26/12/1076
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https://www.mdpi.com/1099-4300/26/12/1076

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