13 citations · 25 across the 11 of their papers we have counts for
11 papers
Mean-field Variational Bayes for Sparse Probit Regression
Augusto Fasano, Giovanni Rebaudo
We consider Bayesian variable selection for binary outcomes under a probit link with a spike-and-slab prior on the regression coefficients. Motivated by the computational challenge…
Scalable expectation propagation for generalized linear models
Niccolò Anceschi, Augusto Fasano, Beatrice Franzolini +1
Generalized linear models (GLMs) arguably represent the standard approach for statistical regression beyond the Gaussian likelihood scenario. When Bayesian formulations are employe…
Expectation propagation for the smoothing distribution in dynamic probit
Niccolò Anceschi, Augusto Fasano, Giovanni Rebaudo
The smoothing distribution of dynamic probit models with Gaussian state dynamics was recently proved to belong to the unified skew-normal family. Although this is computationally t…
Efficient computation of predictive probabilities in probit models via expectation propagation
Augusto Fasano, Niccolò Anceschi, Beatrice Franzolini +1
Binary regression models represent a popular model-based approach for binary classification. In the Bayesian framework, computational challenges in the form of the posterior distri…
Efficient expectation propagation for posterior approximation in high-dimensional probit models
Augusto Fasano, Niccolò Anceschi, Beatrice Franzolini +1
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 l…
Bayesian conjugacy in probit, tobit, multinomial probit and extensions: A review and new results
Niccolò Anceschi, Augusto Fasano, Daniele Durante +1
A broad class of models that routinely appear in several fields can be expressed as partially or fully discretized Gaussian linear regressions. Besides including basic Gaussian res…
Bayesian Inference for the Multinomial Probit Model under Gaussian Prior Distribution
Augusto Fasano, Giovanni Rebaudo, Niccolò Anceschi
Multinomial probit (mnp) models are fundamental and widely-applied regression models for categorical data. Fasano and Durante (2022) proved that the class of unified skew-normal di…
Variational Inference for the Smoothing Distribution in Dynamic Probit Models
Augusto Fasano, Giovanni Rebaudo
Recently, Fasano, Rebaudo, Durante and Petrone (2019) provided closed-form expressions for the filtering, predictive and smoothing distributions of multivariate dynamic probit mode…
A Class of Conjugate Priors for Multinomial Probit Models which Includes the Multivariate Normal One
Augusto Fasano, Daniele Durante
Multinomial probit models are routinely-implemented representations for learning how the class probabilities of categorical response data change with p observed predictors. Althoug…
Scalable and Accurate Variational Bayes for High-Dimensional Binary Regression Models
Augusto Fasano, Daniele Durante, Giacomo Zanella
Modern methods for Bayesian regression beyond the Gaussian response setting are often computationally impractical or inaccurate in high dimensions. In fact, as discussed in recent…
A closed-form filter for binary time series
Augusto Fasano, Giovanni Rebaudo, Daniele Durante +1
Non-Gaussian state-space models arise in several applications, and within this framework the binary time series setting provides a relevant example. However, unlike for Gaussian st…