Analytic Methods for Cosmological Likelihoods

Details Analytic Methods for Cosmological Likelihoods Analytic Methods for Cosmological Likelihoods

2010-03-04

arxiv.org/abs/1003.1136v1

Astronomy and Astrophysics

Astrophysics

Cosmology and Extragalactic Astrophysics

12 pages, 4 figures, submitted to MNRAS

Scientific paper

We present general, analytic methods for Cosmological likelihood analysis and solve the "many-parameters" problem in Cosmology. Maxima are found by Newton's Method, while marginalization over nuisance parameters, and parameter errors and covariances are estimated by analytic marginalization of an arbitrary likelihood function with flat or Gaussian priors. We show that information about remaining parameters is preserved by marginalization. Marginalizing over all parameters, we find an analytic expression for the Bayesian evidence for model selection. We apply these methods to data described by Gaussian likelihoods with parameters in the mean and covariance. This method can speed up conventional likelihood analysis by orders of magnitude when combined with Monte-Carlo Markov Chain methods, while Bayesian model selection becomes effectively instantaneous.

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