modifying priors when posteriors don't agree with intuition
I'm a big fan of Andrew Gelman, but the quote below from a 2006 article worries me
blog comments powered by Disqus
We view any noninformative or weakly-informative prior distribution as inherently provisional--after the model has been fit, one should look at the posterior distribution and see if it makes sense. If the posterior distribution does not make sense, this implies that additional prior knowledge is available that has not been included in the model, and that contradicts the assumptions of the prior distribution that has been used. It is then appropriate to go back and alter the prior distribution to be more consistent with this external knowledge.Isn't this exactly the potential problem that fuels anti-Bayesian sentiment?
blog comments powered by Disqus