I’m relatively new to Bayesian methods and I’m looking for a prior for parameters that should generally be strongly discouraged from becoming negative (but it should not be impossible) and the most probable value should be around 0.1 or so and remain about as probable until value 1.0 or so.
I’m picturing a kind of truncated sigmoid function. Actually the CDF of a normal distribution seems like a reasonable option to me but that would be an improper prior, which I guess would not be a good idea?
Any suggestions for a good prior? I’ve also been looking into the inverse gamma distribution but I’m wondering if that would be a good option. I don’t really have a theoretical reason to prefer 0.1 over 0.5 for instance. Mostly I just know that anything approaching and below zero should become increasingly less probable.