Efficient computation of predictive probabilities in probit models via expectation propagation

TL;DR

This paper introduces a novel method for efficiently computing predictive probabilities in Bayesian probit models using expectation propagation, providing a closed-form solution that improves upon existing techniques.

Contribution

The paper derives a closed-form expression for predictive probabilities in Bayesian probit models via expectation propagation, enhancing computational efficiency.

Findings

Closed-form expression for predictive probabilities

Improved computational efficiency over existing methods

Validated improvements through simulation study

Abstract

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.