Olga, I'm having a very difficult time understanding what you are trying to say and do. Lengthy emails aren't necessarily helpful. Please try to state simple questions and post simple examples. Please understand that you want us to read your messages, understand them, and respond to them with something other than requests for clarification. The risk for you is that we'll simply give up asking for clarification after a few attempts. I've made a few attempts already. Much of what you say is really very confusing. Some of it is just wrong (Bayesian approaches certainly do not dictate what "space" to use). It's also simply too much to reply to all of it. It is clear that you're trying to do some fitting in k-space AND have a meaningful statistical treatment. Stop doing this. Fit in R-space if you want to do any meaningful statistical analysis. You also want to do something "Bayesian" because some fit "didn't work", which you don't actually define. If a fit doesn't give meaningful results, it seems likely to me that the data simply doesn't support the variables you're trying to fit. I doubt a Bayesian approach is likely to help. Of course, you can use priors to skew the fit to expected values for the parameters, but that's probably not very different than just constraining parameter values. If you have any questions about the code or statistical treatments, or want to use something supported or for which development can possibly progress, use Larch. Ifeffit is no longer supported and won't be developed further. Specifically for you, Ifeffit may very well have bugs in fitting in k-space -- it's never been a good idea to fit in k-space, so this option was never tested well. If you're interested in adding restraints or priors or other Bayesian tools to Larch, that would be great. The fitting code in Larch is already far superior to that in Ifeffit, but there is always room for improvement. There are a handful of Python tools for Bayesian analysis, and also for using MCMC methods. These are well-supported and tested, and would not be hard to incorporate into Larch. Cheers, --Matt