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📄 Abstract
Abstract: We propose a novel Bayesian nonparametric classification model that combines
a Gaussian process prior for the latent function with a Dirichlet process prior
for the link function, extending the interpretative framework of de Finetti
representation theorem and the construction of random distribution functions
made by Ferguson (1973). This approach allows for flexible uncertainty modeling
in both the latent score and the mapping to probabilities. We demonstrate the
method performance using simulated data where it outperforms standard logistic
regression.