I am looking for a good way to visualize predictive posterior for binomial regression. In my case y[i]~binomial_logit(N, alpha+betax[i]) - or in vectorized form y~binomial_logit(N, alpha+xbeta) where x is a matrix and represents 10+ predictors for each observation, beta is a vector.

Is this a visualization for posterior predictive checking (i.e., comparing predictions to observed binomial counts) or for visualizing out-of-sample predictions? More specifically, what are you interested in showing in (learning from) this visualization? There’s tons of ways to visualize these things, so knowing more about your purpose would be helpful.

Is it as simple as having in generated quantities y_pred[i]=binomial_logit_rng(N, alpha+beta*x[i]) and then taking 90% credible region of y_pred and comparing with observed binomial counts?