I'm very sorry that I'm long. That is because I didn't suppose to explain the approach itself, but to understand what is with formulae of statistics in IFEFFIT. I understood that you say that k-space is a problem because you didn't rely on it. I thought it is not a good idea for IFEFFIT statistic, not Bayes. I would like to know what you mean of 'Bayes doesn't dictate space', I didn't understand it from papers and books, may be you will help me find where it is said and what is math? I really didn't know anything of it, there was analysis of original spectra in all cases. I have statistically good fits on this compounds, but with constraints, and Bayesian approach could help decide what parametra are nonmeaningful. It is hard to decide between models without it. I will try a test in R-space now, and to simplify the test, if you think, but not sure how to use it to the end. Is it that I should use epsilon_R and simply use FT-transformed dat and model and Bayes statistics will be the same? I'm not sure noise can be treated as constant in this case, you see, it depends on k-weighting and so on. Uncertainty is from noise (that can be thought as Gaussian) and mu0, and it is not quite understandable how to add mu0 co-fitting than. Thank you for help, Olga Kashurnikova