cosmography, dust mapping, null data, discrete optimization

In a very full day, I learned about quasar-absorption-line-based mapping of the density field in large volumes of the Universe from K. G. Lee (MPIA), I discussed non-parametric methods for inferring the three-dimensional dust map in the Milky Way from individual-star measurements with Richard Hanson (MPIA), I was impressed by work by Beth Biller (MPIA) that constrains the exoplanet population by using the fact (datum?) that there are zero detections in a large direct-detection experiment, and I helped Beta Lusso (MPIA) get her discrete optimization working for maximum-likelihood quasar SED fitting. On the latter, we nailed it (Lusso will submit the paper tomorrow of course) but before nailing it we had to do a lot of work choosing the set of models (discrete points) over which fitting occurred. This reminds me of two of my soap-box issues: (a) Construction of a likelihood function is as assumption-laden as any part of model fitting, and (b) we should be deciding which models to include in problems like this using hierarchical methods, not by fitting, judging, and trimming by hand. But I must say that doing the latter does help one develop intuition about the problem! If nothing else, Lusso and I are left with a hell of a lot of intuition.

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