Bayesian Analysis

Using Stacking to Average Bayesian Predictive Distributions

Yuling Yao, Aki Vehtari, Daniel Simpson, and Andrew Gelman

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Abstract

Bayesian model averaging is flawed in the M-open setting in which the true data-generating process is not one of the candidate models being fit. We take the idea of stacking from the point estimation literature and generalize to the combination of predictive distributions. We extend the utility function to any proper scoring rule and use Pareto smoothed importance sampling to efficiently compute the required leave-one-out posterior distributions. We compare stacking of predictive distributions to several alternatives: stacking of means, Bayesian model averaging (BMA), Pseudo-BMA, and a variant of Pseudo-BMA that is stabilized using the Bayesian bootstrap. Based on simulations and real-data applications, we recommend stacking of predictive distributions, with bootstrapped-Pseudo-BMA as an approximate alternative when computation cost is an issue.

Article information

Source
Bayesian Anal., Advance publication (2018), 28 pages.

Dates
First available in Project Euclid: 16 January 2018

Permanent link to this document
https://projecteuclid.org/euclid.ba/1516093227

Digital Object Identifier
doi:10.1214/17-BA1091

Keywords
Bayesian model averaging model combination proper scoring rule predictive distribution stacking Stan

Rights
Creative Commons Attribution 4.0 International License.

Citation

Yao, Yuling; Vehtari, Aki; Simpson, Daniel; Gelman, Andrew. Using Stacking to Average Bayesian Predictive Distributions. Bayesian Anal., advance publication, 16 January 2018. doi:10.1214/17-BA1091. https://projecteuclid.org/euclid.ba/1516093227


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Bayesian Analysis

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