high-energy astrophysics; inference without likelihoods

[Lull in posting because of vacation in Quebec. Slept one (exceedingly cold) night in an actual, real-life igloo!]

At lunchtime in the NYU Center for Data Science there was a great talk by Daniela Huppenkothen about x-ray and gamma-ray astrophysics, for non-astronomers. She talked about imaging, spectroscopy, and time series, with a focus on the latter. She did a great job explaining the differences between astrophysics and other data-science domains. At the end there were good questions from (among others) neural scientist Bijan Peseran, who (comparing perhaps to his own domain) was interested in non-trivial time correlations among photon events. After all, neurons are all about non-trivial time correlations in spike trains.

Earlier in the day, Foreman-Mackey and I spoke about K2 projects and exoplanet population projects. The plan is to try some likelihood-free inference; we spent some time talking about technical details. In likelihood-free inference (ABC) one performs repeated simulations of the data; there are fundamental parameters, and then there are (usually) also random-number draws. We might want to sample separately in these, in some Gibbs-like way. Thanks to Brewer for getting us thinking along these lines.

No comments:

Post a Comment