[o09rdk]

I was kind of wondering about this after reading the passage.

On the one hand the traditional Bayesian response is something like "yes, we're making our prior assumptions explicit and then incorporating that into a formal inferential paradigm."

However, this prior is being used to bias the estimates, rather than to avoid the bias. That is, it would be akin to Dunnington taking the current estimates of e/m and using that to shape any new estimates from data. The argument is then that at least he was being explicit about his biases and how they are used to make an estimate.

This has always seemed backwards to me, though. It seems what is more defensible is to use a formal theory about how prior biases affect estimates, and then to leverage that theory to minimize biases. This is basically the idea of the reference prior, to estimate things such that any role of the prior is minimized in an information-theoretic sense. This seems more analogous to what Dunnington was doing.

I really wish reference priors were more widespread, although they can be computationally pretty hefty. It's one of my hopes that quantum computing might make these types of approaches more feasible in general.