Hi Martin,
Thanks for your comment. You may be right, I find it difficult to picture how MCMC would deal with such a parameter.
So would you suggest I include such background knowledge as a prior then? I’m guessing that would be the most correct approach but it may not be feasible given the number of parameters in my model.
Alternatively I could perhaps fix a parameter that I already know the distribution of to its mean during MCMC but then also sample from it when running posterior predictive simulations (next to the posterior identified by MCMC which then does not include that specific parameter). That may significantly bias my results though.
What do you think?