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June 2013 Comment on Article by Müller and Mitra
Peter D. Hoff
Bayesian Anal. 8(2): 311-318 (June 2013). DOI: 10.1214/13-BA811B


Due to their great flexibility, nonparametric Bayes methods have proven to be a valuable tool for discovering complicated patterns in data. The term “nonparametric Bayes” suggests that these methods inherit model-free operating characteristics of classical nonparametric methods, as well as coherent uncertainty assessments provided by Bayesian procedures. However, as the authors say in the conclusion to their article, nonparametric Bayesian methods may be more aptly described as “massively parametric.” Furthermore, I argue that many of the default nonparametric Bayes procedures are only Bayesian in the weakest sense of the term, and cannot be assumed to provide honest assessments of uncertainty merely because they carry the Bayesian label. However useful such procedures may be, we should be cautious about advertising default nonparametric Bayes procedures as either being “assumption free” or providing descriptions of our uncertainty. If we want our nonparametric Bayes procedures to have a Bayesian interpretation, we should modify default NP Bayes methods to accommodate real prior information, or at the very least, carefully evaluate the effects of hyperparameters on posterior quantities of interest.


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Peter D. Hoff. "Comment on Article by Müller and Mitra." Bayesian Anal. 8 (2) 311 - 318, June 2013.


Published: June 2013
First available in Project Euclid: 24 May 2013

zbMATH: 1329.62168
MathSciNet: MR3066941
Digital Object Identifier: 10.1214/13-BA811B

Rights: Copyright © 2013 International Society for Bayesian Analysis


Vol.8 • No. 2 • June 2013
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