As always, Wednesdays are research-filled days. At the beginning of the day, Bedell (Flatiron) reminded me that I have a boat-load of writing to do on our joint projects, and the urgency is high: We will have a submittable paper by next week if all goes well. So I printed out some old, old text to revise.
One of the research highlights of the day was a joint conversation with Anderson (Flatiron), Leistedt (NYU), and David Blei (Columbia), about building a big, self-consistent, probabilistically justifiable, statistically isotropic model of the Milky Way dust. This is a project we have been kicking around for years now, but never really got serious about. My thoughts were sharpened in the last two summers at MPIA by Sara Rezaei Kh (MPIA), with whom I did a bit of Gaussian process work. But that isn't really tractable for large data, and can't really deal with non-trivial likelihoods (like distance uncertainties, and covariant distance and extinction uncertainties). It looks like we might try black-box variational inference on the problem. This won't give exact inferences but it might be able to handle the size and complexity of the data we have.
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