Bambi logistic regression, prior and posterior distributions of probabilities

Hi everyone,

I was wondering: when doing a logistic regression in bambi (using family=“bernoulli” as the likelihood), is it possible to easily get prior and posterior distributions of the probabilities passed to the likelihood function or does one need to compute those manually from the prior/posterior distributions of the input variables?

Thanks!

Hi @leomein,

Sorry for the late comment. You can easily get the posterior of the probability of success using the .predict() method in the Model object. Here you have an example.

For the prior you have .prior_predictive(), but it returns values in the scale of the response (0-1 values) not in the scale of the mean.

Let me know if you have any questions about how to use it.