“…Being a probability density, its dimensionality can be easily reduced by integrating out less interesting (nuisance) parameter directions ψ i , yielding an n-dimensional marginal posterior pdf, P mar (θ 1 , ..., θ n |X) ∝ dψ 1 ...dψ m P(θ 1 , ..., θ n , ψ 1 ..., ψ m |X) , (A. 4) which is more amenable to visual presentation if n = 1, 2, and can be used to construct constraints on the remaining parameters. We employ a modified version of the generic Metropolis-Hastings sampler [108,109] included in the public MCMC code CosmoMC [110,111], to sample the posterior over the full parameter space.…”