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Found 6 papers, 4 papers with code
Expectation propagation for the smoothing distribution in dynamic probit
The smoothing distribution of dynamic probit models with Gaussian state dynamics was recently proved to belong to the unified skew-normal family.
Efficient computation of predictive probabilities in probit models via expectation propagation
Binary regression models represent a popular model-based approach for binary classification.
Efficient expectation propagation for posterior approximation in high-dimensional probit models
Bayesian binary regression is a prosperous area of research due to the computational challenges encountered by currently available methods either for high-dimensional settings or large datasets, or both.
Entropy regularization in probabilistic clustering
The proposed estimator is equivalent to a post-processing procedure that reduces the number of sparsely-populated clusters and enhances interpretability.
Bayesian Inference for the Multinomial Probit Model under Gaussian Prior Distribution
This allows to obtain simplified expressions for the parameters of the posterior distribution and an alternative derivation for the variational algorithm that gives a novel understanding of the fundamental results in Fasano and Durante (2022) as well as computational advantages in our special settings.
Clustering consistency with Dirichlet process mixtures
Dirichlet process mixtures are flexible non-parametric models, particularly suited to density estimation and probabilistic clustering.
