Gaussianisation for fast and accurate inference from cosmological data

Understand

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 generalisations 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 contained in MCMC samples.
• Further, the model evidence integral (i.e.