where α, β = r 1 , r 2 label the parameters. This is, however, only a lower bound on the error by virtue of the Cramér-Rao bound which states that errors are bounded from below by the Fisher information matrix. The equality holds for a Gaussian posterior (or an affine parameter space) and it converges to the equality as 1/n, where n is the number of (independent) measurements taken. Thus, if there are only few data points available, non-Gaussian features in the posterior become present. Furthermore, the limitation r 1,2 > 0 will skew the posterior towards larger parameter values. If we normalize p(θ| f 2 , F 2 ) a 100q%-confidence intervals are then given by the contour C of p(θ| f 2 , F 2 ) satisfying: