On the last day of #LGAstat, Ivezic (UW) spoke about LSST and his ideas about where we aren't yet ready. The project is funded to generate the data, but not to do all the science with the data, so a lot of the things we want from LSST we will have to do ourselves and figure out ourselves. The cool thing is that the project has a "Level 3" products plan that will make it possible for people outside the project team to contribute code and catalogs and measurements and outputs to the pipelines. Ivezic made an interesting suggestion: Even if you aren't doing insane Bayesian inferences, it is important to understand Bayesian reasoning (I really think he meant "probabilistic reasoning") in order to be clear about (and communicate clearly about) your data-analysis assumptions. I agree!
Hargis (Haverford) and Bechtol (UWM) and Sand (Texas Tech) talked about dwarf galaxy detections in various data sets and expected numbers and so on. Bechtol's methods are probabilistic mixture models and are performing incredibly well: The DES has already found many new ultra-faint dwarf companions to the Milky Way.
Wetzel (Caltech) and Tollerud (Yale) gave nice talks that continued themes from earlier in the week about comparing simulations to data: They are importance-sampling (with the likelihood function) prior samples (from n-body simulations) to understand the infall times of Milky-Way satellites. They are both data and theory starved (so I think their samplings aren't converged in any sense) but they get really informative posterior information about infall times. This method (importance-sampling of n-body simulation samples) is looking incredibly productive as a new technique. Props to Busha and Marshall and others who pioneered this!
I had to miss a few talks because of work stuff, including an interesting contribution by Walker (CMU) that looked absolutely great (about probabilistic stellar parameter estimation). As usual, the above summary is very unfair and incomplete! Thanks to Loebman (Michigan) and Nidever (Michigan) for a great meeting.
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