According to the maximum entropy principle a uniform distribution should be used if all you know is your parameter falls in some range and its a location type parameter. If the mean is constrained (which it seems it is based on your prior for # Gives contrast roughly 20% C = pm.Beta("C", alpha=20, beta=80)), you should use a truncated exponential distribution - as that's the maximum entropy distribution given your constraints.
According to the maximum entropy principle a uniform distribution should be used if all you know is your parameter falls in some range and its a location type parameter. If the mean is constrained (which it seems it is based on your prior for
# Gives contrast roughly 20% C = pm.Beta("C", alpha=20, beta=80)), you should use a truncated exponential distribution - as that's the maximum entropy distribution given your constraints.