http://arxiv.org/abs/1506.04866
We extent the previously published DALI-approximation for likelihoods to cases of a parameter dependent covariance matrix. The approximation recovers non-Gaussian likelihoods and falls back onto the Fisher matrix approach in the case of Gaussianity. It works with the minimal assumptions of having Gaussian errors on the data, and a covariance matrix that posesses a converging Taylor approximation. The resulting approximation works in cases of severe parameter degeneracies and in cases where the Fisher matrix is singular. It is easily a 1000 times faster than typical Monte Carlo Markov Chain runs. Two example applications to cases of extremely non-Gaussian likelihoods are presented – one demonstrates how the method succeeds in reconstructing completely a ring-shaped likelihood. A public code is released on github.
E. Sellentin
Wed, 17 Jun 15
20/47
Comments: 6 pages, 2 figures