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The data torrent unleashed by current and upcoming astronomical surveysdemands scalable analysis methods. Many machine learning approaches scale well,but separating the instrument measurement from the physical effects ofinterest, dealing with variable errors, and deriving parameter uncertainties isoften an after-thought. Classic forward-folding analyses with Markov ChainMonte Carlo or Nested Sampling enable parameter estimation and modelcomparison, even for complex and slow-to-evaluate physical models. However,these approaches require independent runs for each data set, implying anunfeasible number of model evaluations in the Big Data regime. Here I present anew algorithm, collaborative nested sampling, for deriving parameterprobability distributions for each observation. Importantly, the number ofphysical model evaluations scales sub-linearly with the number of data sets,and no assumptions about homogeneous errors, Gaussianity, the form of the modelor heterogeneity/completeness of the observations need to be made.Collaborative nested sampling has immediate application in speeding up analysesof large surveys, integral-field-unit observations, and Monte Carlosimulations.

Collaborative Nested Sampling: Big Data vs. complex physical models