Hi am after some advice. I often find that when setting up a model I want to fix a parameters during model exploration. An example of this is would be exploring if incorporating auto correlation in a Covariance matrix is better than the independence assumption \rho = 0 vs \rho \neq 0. I have used three methods
move the parameter declaration from the parameter section to the data section and recompile. However this ends up creating many models and messy directory.
have data values for bounds of a parameter, and set the upper and lower bound to the value you want to fix the parameter at.
Have a ridiculous prior that basically constrains a parameter to a fixed value
I worry that 2 & 3 will effect sampling of the posterior. Are there better implementation than these? Apologies if I have missed an obvious solution.