Bayesian variable selection [off again]
From the post:
As indicated a few weeks ago, we have received very encouraging reviews from Bayesian Analysis about our [Gilles Celeux, Mohammed El Anbari, Jean-Michel Marin and myself] our comparative study of Bayesian and non-Bayesian variable selections procedures (“Regularization in regression: comparing Bayesian and frequentist methods in a poorly informative situation“) to Bayesian Analysis. We have just rearXived and resubmitted it with additional material and hope this is the last round. (I must acknowledge a limited involvement at this final stage of the paper. Had I had more time available, I would have liked to remove the numerous tables and turn them into graphs…)
If you are not conversant in Bayesian thinking and recent work, this paper is going to be … difficult. Despite just having gotten past the introduction and looking references to help with part 2, I think it will be a good intellectual exercise and important for your use of Bayesian models in the future. Two very good reasons to spend the time to understand this paper.
Or to put it another way, the world is non-probabilistic only when viewed with a certain degree of coarseness. How useful a coarse view is, varies from circumstance to circumstance. If you don’t have the capability to use a probabilistic view, you will be limited to a coarse one. (Neither better than the other, but having both seems advantageous to me.)