hierarchical model for the redshift distribution

On the airplane home from MPIA (boo hoo!) I wrote the shortest piece of code I could that can take interim posterior p(z) redshift probability distributions from a set of galaxies and produce N(z) (and maybe other one-point statistics). I can make pathological cases in which there are terrible photometric-redshift outliers that are structured to cause havoc for N(z). But as long as you have a good generative model (and that is a big ask, I hate to admit), and as long as the providers of the p(z) information also provide the effective prior on z that was used to generate the p(z)s (another big ask, apparently), you can infer the true N(z) surprisingly accurately. This is work with Alex Malz and Boris Leistedt.

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