31 citations · 78 across the 14 of their papers we have counts for
15 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…
Multivariate Species Sampling Models
Beatrice Franzolini, Antonio Lijoi, Igor Prünster +1
Species sampling processes have long served as the fundamental framework for modeling random discrete distributions and exchangeable sequences. However, data arising from distinct…
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…
Entropy regularization in probabilistic clustering
Beatrice Franzolini, Giovanni Rebaudo
Bayesian nonparametric mixture models are widely used to cluster observations. However, one major drawback of the approach is that the estimated partition often presents unbalanced…
Graph-Aligned Random Partition Model (GARP)
Giovanni Rebaudo, Peter Mueller
Bayesian nonparametric mixtures and random partition models are powerful tools for probabilistic clustering. However, standard independent mixture models can be restrictive in some…
Bayesian Mixed Multidimensional Scaling for Auditory Processing
Giovanni Rebaudo, Fernando Llanos, Bharath Chandrasekaran +1
The human brain distinguishes speech sounds by mapping acoustic signals into a latent perceptual space. This space can be estimated via multidimensional scaling (MDS), preserving t…
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…
Clustering consistency with Dirichlet process mixtures
Filippo Ascolani, Antonio Lijoi, Giovanni Rebaudo +1
Dirichlet process mixtures are flexible non-parametric models, particularly suited to density estimation and probabilistic clustering. In this work we study the posterior distribut…
Flexible clustering via hidden hierarchical Dirichlet priors
Antonio Lijoi, Igor Prünster, Giovanni Rebaudo
The Bayesian approach to inference stands out for naturally allowing borrowing information across heterogeneous populations, with different samples possibly sharing the same distri…
Separate Exchangeability as Modeling Principle in Bayesian Nonparametrics
Giovanni Rebaudo, Qiaohui Lin, Peter Mueller
We argue for the use of separate exchangeability as a modeling principle in Bayesian nonparametric (BNP) inference. Separate exchangeability is \emph{de facto} widely applied in th…
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 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…