# 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…

## 77 Citations

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- Computer ScienceProceedings of the National Academy of Sciences
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- Computer ScienceThe Astrophysical Journal
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We present and apply Gaussbock, a new embarrassingly parallel iterative algorithm for cosmological parameter estimation designed for an era of cheap parallel-computing resources. Gaussbock uses…

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