13 citations · 25 across the 11 of their papers we have counts for
5 papers · 1 filter
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…
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…