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