Binary regression models represent a popular model-based approach for binary
classification. In the Bayesian framework, computational challenges in the form
of the posterior distribution motivate still-ongoing fruitful research. Here,
we focus on the computation of predictive probabilities in Bayesian probit
models via expectation propagation (EP). Leveraging more general results in
recent literature, we show that such predictive probabilities admit a
closed-form expression. Improvements over state-of-the-art approaches are shown
in a simulation study