I was reading about methods that people have used for fractional regression models (regression models where the outcome is between [0,1], and one popular method is the fractional logit model. This is a quasi-bernoulli likelihood specified as:

If we were to put a prior on \beta, I was wondering what the community’s thoughts are on implementing this model in Stan is, or more generally, if we can use these quasi-likelihoods in Bayesian inference.