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.
Fasano, A., Anceschi, N., Franzolini, B., Rebaudo, G. (2023). Efficient computation of predictive probabilities in probit models via expectation propagation. In P. Coretto, G. Giordano, M. La Rocca, M.L. Parrella, C. Rampichini (a cura di), CLADAG 2023 - Book of abstracts and short papers (pp. 449-452). Pearson.
Efficient computation of predictive probabilities in probit models via expectation propagation
Franzolini, Beatrice;
2023
Abstract
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.
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