ΛCDM model, P(k) = A

_{s}k C(k), where C(k) includes two extra free

parameters. We know from previous analysis that the spectra is not

sensitive to huge values of the free parameters. Therefore, for huge values

of these parameters, there is always good fit to the data

(likelihood/likelihood_max ≈ 1). The

question is: How can we determine the confidence interval for a given

parameter (i.e, the value X for the parameter for which I can say

"The value of the parameter is greater

that X with 68% confidence") when the likelihood does not fall to zero at

infinity (hence it is not normalized)?.