highly informative prior

Tsalmantza and I pair-coded two parts of our spectral modeling software (in R it has limitations, but it is open-source): (1) We set things up to include a Gaussian-process-like smoothness prior. This ended up being trivial, but it looked bad at first because it makes a block-diagonal matrix non-block-diagonal. (2) We worked through the bugs in the highly informative prior we are trying out on our quasar-redshift project: Do we measure the redshifts of quasars better if we insist that the redshift be measured by a spectrum that looks very much like an already-seen quasar? The prior is what I would call a mixture-of-archetypes. After we finished the latter part, the answer looks like it is going to be yes. If so, this is a win for Bayes; this project directly competes a marginalize-out-nuisances (with a highly informative prior) against a optimize-everything method; the first is very Bayesian (extreme, you might say) and the second is very frequentist.

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