# Bayesian Predictive Decision Synthesis

arXiv: https://arxiv.org/abs/2206.03815 · HTML: https://mlsanity.com/paper/2206.03815/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2022.

## Built on by (most cited first, 4 of 4)

- [Bayesian Portfolio Optimization by Predictive Synthesis](https://mlsanity.com/paper/2510.07180/) (2025)
- [When Is Generalized Bayes Bayesian? A Decision-Theoretic Characterization of Loss-Based Updating](https://mlsanity.com/paper/2602.01573/) (2026)
- [Predictive Density Combination Using a Tree-Based Synthesis Function](https://mlsanity.com/paper/2311.12671/) (2023)
- [General Bayesian Policy Learning](https://mlsanity.com/paper/2602.23672/) (2026)

## Abstract (arXiv)

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.