are pseudopriors required in Bayesian model selection?

An interesting question from X validated about constructing pseudo-priors for Bayesian model selection. Namely, how useful are these for the concept rather than the implementation? The only case where I am aware of pseudo-priors being used is in Bayesian MCMC algorithms such as Carlin and Chib (1995), where the distributions are used to complement the posterior distribution conditional on a single model (index) into a joint distribution across all model parameters. The trick of this construction is that the pseudo-priors can be essentially anything, including depending on the data as well. And while the impact the ability of the resulting Markov chain to move between spaces, they have no say on the resulting inference, either when choosing a model or when estimating the parameters of a chosen model. The concept of pseudo-priors was also central to the mis-interpretations found in Congdon (2006) and Scott (2002). Which we reanalysed with Jean-Michel Marin in Bayesian Analysis (2008) as the distinction between model-based posteriors and joint pseudo-posteriors.

the last argument of drivers

When vaguely listening to the national public radio France Inter last night, while cooking dinner, I heard Patrick Septiers, president of le conseil départemental de Seine et Marne, express his (electorate catering) opposition to the new 80km/h speed limit on national and departmental roads on the most rational (!) argument that delivery trucks drove at that speed already and hence that the speed limit would “force” car drivers to break the law to pass trucks. Along with similarly rational claims to have each department regulate its speed limits on the basis it was financing most roads. (I had another illustration of the rationality above when walking by a big SUV this morning, with a large sticker against wind farms.)

the last argument of kings [jatp]