Gaussianization for fast and accurate inference from cosmological data

@article{Schuhmann2016GaussianizationFF,
  title={Gaussianization for fast and accurate inference from cosmological data},
  author={Robert L. Schuhmann and Benjamin Joachimi and Hiranya V. Peiris},
  journal={Monthly Notices of the Royal Astronomical Society},
  year={2016},
  volume={459},
  pages={1916-1928}
}
We present a method to transform multivariate unimodal non-Gaussian posterior probability densities into approximately Gaussian ones via non-linear mappings, such as Box–Cox transformations and generalizations thereof. This permits an analytical reconstruction of the posterior from a point sample, like a Markov chain, and simplifies the subsequent joint analysis with other experiments. This way, a multivariate posterior density can be reported efficiently, by compressing the information… Expand

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