Pragmatic Approaches to Modeling Multi-Dimensional Astronomical Data

Metro Toronto Convention Centre 

I discuss examples of how we at the CHASC Astrostatistics Collaboration have done several analyses of high-energy astronomical data (spatial, spatial+temporal, spatial+spectral, spectral+temporal, spatial+spectral+temporal) tailored to the specific astronomical problems that are of interest. The differences in approaches highlight what compromises were necessary to achieve progress and what trade-offs were needed to maximize the utility of the results. The methods include Bayesian, frequentist, computer vision, and machine learning techniques, several used in combination.