(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)

Ryan Giordano, Tamara Broderick, and Michael Jordan write:

In Bayesian analysis, the posterior follows from the data and a choice of a prior and a likelihood. One hopes that the posterior is robust to reasonable variation in the choice of prior, since this choice is made by the modeler and is often somewhat subjective. A different, equally subjectively plausible choice of prior may result in a substantially different posterior, and so different conclusions drawn from the data. . . .

To which I say:

,s/choice of prior/choice of prior and data model/g

Yes, the choice of data model (from which comes the likelihood) is made by the modeler and is often somewhat subjective. In those cases where the data model is *not* chosen subjectively by the modeler, it is typically chosen implicitly by convention, and there is even more reason to be concern about robustness.

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