deconvolution and hierarchical Bayes

I wrote english prose and LaTeX equations like the wind on our brand-new exoplanet project. In particular, I spent some time working out the difference between hierarchical bayesian approaches to distribution estimation and deconvolution or forward-modeling approaches. There is a lot of overlap, but the key difference is that if all you care about is the distribution itself, in the Bayesian approaches you integrate out all of the individual measurements (which, in this context, should be thought of as fits to more "raw" data). That is, if you are deconvolving (forward modeling) you are trying to explain the individual-object fit results; if you are hierarchical Bayesian, you are trying to obliterate them. As I wrote text, Myers wrote code, and Lang (who came into town) worked on image modeling in preparation for the NIPS deadline.

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