How do we write the likelihood function for an observed variable that can only take values between 0-1 and is continuous. I am trying to do a measurement error model in which one of the input variables is a proportion and has a standard error associated with it. As such it can only take values between 0-1 but the standard error on the quantity may put it out of that range. Any reference would be highly appreciated.
Depending on the details, either
pm.Beta seems like it would work.
Thanks for answering. In general how do we constrain the likelihood to be between a given range?
For uniform and beta priors this is guaranteed. For others, you can use pm.Bound() to force it between a lower and upper bound and have the machinery take care of normalization.