Massive optimal data compression and density estimation for scalable, likelihood-free inference in cosmology

@article{Alsing2018MassiveOD,
  title={Massive optimal data compression and density estimation for scalable, likelihood-free inference in cosmology},
  author={Justin Alsing and Benjamin Dan Wandelt and Stephen M. Feeney},
  journal={Monthly Notices of the Royal Astronomical Society},
  year={2018},
  volume={477},
  pages={2874-2885}
}
Many statistical models in cosmology can be simulated forwards but have intractable likelihood functions. Likelihood-free inference methods allow us to perform Bayesian inference from these models using only forward simulations, free from any likelihood assumptions or approximations. Likelihood-free inference generically involves simulating mock data and comparing to the observed data; this comparison in data space suffers from the curse of dimensionality and requires compression of the data to… 
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