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.

翻译：二元回归模型是二分类问题中一种流行的基于模型的方法。在贝叶斯框架下，后验分布的计算挑战持续推动着富有成效的研究。本文重点关注通过期望传播（EP）计算贝叶斯probit模型中的预测概率。利用近期文献中的更一般性结论，我们证明此类预测概率具有闭式表达式。模拟研究表明，该方法相较于现有最优方法具有显著改进。