5 citations · 13 across the 9 of their papers we have counts for
9 papers
Laplace and skew-Laplace approximations for Dirichlet process mixture posterior density
Beatrice Franzolini, Francesco Pozza
Posterior inference for Dirichlet process mixture models is analytically intractable and typically relies on Markov chain Monte Carlo methods, which can become computationally proh…
Complexity bounds for Dirichlet process slice samplers
Beatrice Franzolini, Francesco Gaffi
Slice sampling is a standard Monte Carlo technique for Dirichlet process (DP)-based models, widely used in posterior simulation. However, formal assessments of the scalability of p…
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…
Nonparametric priors with full-range borrowing of information
Filippo Ascolani, Beatrice Franzolini, Antonio Lijoi +1
Modeling of the dependence structure across heterogeneous data is crucial for Bayesian inference since it directly impacts the borrowing of information. Despite the extensive advan…
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…
Model Selection for Maternal Hypertensive Disorders with Symmetric Hierarchical Dirichlet Processes
Beatrice Franzolini, Antonio Lijoi, Igor Prünster
Hypertensive disorders of pregnancy occur in about 10% of pregnant women around the world. Though there is evidence that hypertension impacts maternal cardiac functions, the relati…