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Bayesian predictive decision synthesis
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DOI：10.1093/jrsssb/qkad109.png)
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Abstract
En 中文
Decision-guided perspectives on model uncertainty expand traditional statistical thinking about managing, comparing, and combining inferences from sets of models. Bayesian predictive decision synthesis (BPDS) advances conceptual and theoretical foundations, and defines new methodology that explicitly integrates decision-analytic outcomes into the evaluation, comparison, and potential combination of candidate models. BPDS extends recent theoretical and practical advances based on both Bayesian predictive synthesis and empirical goal-focused model uncertainty analysis. This is enabled by the development of a novel subjective Bayesian perspective on model weighting in predictive decision settings. Illustrations come from applied contexts including optimal design for regression prediction and sequential time series forecasting for financial portfolio decisions.
Keywords:
decision-guided model weighting
entropic tilting
generalised Bayesian updating
model uncertainty
optimal design
portfolio decisions
Journal
J
IF:
3.6
Papers:
1.5K
Citations:
3.2W