108 citations · 564 across the 27 of their papers we have counts for
31 papers
Bayesian nonparametric inference for modal missing species and features
Alessandro Colombi, Mario Beraha, Daniele Durante +1
Species and feature sampling problems arise naturally whenever each observed unit is associated with one or more labels from a countable alphabet, and inference focuses on the unob…
Skewed Bernstein-von Mises theorem and skew-modal approximations
Daniele Durante, Francesco Pozza, Botond Szabo
Gaussian approximations are routinely employed in Bayesian statistics to ease inference when the target posterior is intractable. Although these approximations are asymptotically j…
Learning and forecasting of age-specific period mortality via B-spline processes with locally-adaptive dynamic coefficients
Federico Pavone, Sirio Legramanti, Daniele Durante
Although the analysis of human mortality has a well-established history, the attempt to accurately forecast future death-rate patterns for different age groups and time horizons st…
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…
Concentration of discrepancy-based approximate Bayesian computation via Rademacher complexity
Sirio Legramanti, Daniele Durante, Pierre Alquier
There has been increasing interest on summary-free solutions for approximate Bayesian computation (ABC) which replace distances among summaries with discrepancies between the empir…
Bayesian Testing for Exogenous Partition Structures in Stochastic Block Models
Sirio Legramanti, Tommaso Rigon, Daniele Durante
Network data often exhibit block structures characterized by clusters of nodes with similar patterns of edge formation. When such relational data are complemented by additional inf…
Scalable computation of predictive probabilities in probit models with Gaussian process priors
Jian Cao, Daniele Durante, Marc G. Genton
Predictive models for binary data are fundamental in various fields, and the growing complexity of modern applications has motivated several flexible specifications for modeling th…
Extended Stochastic Block Models with Application to Criminal Networks
Sirio Legramanti, Tommaso Rigon, Daniele Durante +1
Reliably learning group structures among nodes in network data is challenging in several applications. We are particularly motivated by studying covert networks that encode relatio…
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…
Bayesian cumulative shrinkage for infinite factorizations
Sirio Legramanti, Daniele Durante, David B. Dunson
There is a wide variety of models in which the dimension of the parameter space is unknown. For example, in factor analysis the number of latent factors is typically not known and…
Conjugate Bayes for probit regression via unified skew-normal distributions
Daniele Durante
Regression models for dichotomous data are ubiquitous in statistics. Besides being useful for inference on binary responses, these methods serve also as building blocks in more com…
Conditionally conjugate mean-field variational Bayes for logistic models
Daniele Durante, Tommaso Rigon
Variational Bayes (VB) is a common strategy for approximate Bayesian inference, but simple methods are only available for specific classes of models including, in particular, repre…
A nested expectation-maximization algorithm for latent class models with covariates
Daniele Durante, Antonio Canale, Tommaso Rigon
We develop a nested EM routine for latent class models with covariates which allows maximization of the full-model log-likelihood and, differently from current methods, guarantees…
Tractable Bayesian Density Regression via Logit Stick-Breaking Priors
Tommaso Rigon, Daniele Durante
There is a growing interest in learning how the distribution of a response variable changes with a set of predictors. Bayesian nonparametric dependent mixture models provide a flex…
Convex Mixture Regression for Quantitative Risk Assessment
Antonio Canale, Daniele Durante, David Dunson
There is wide interest in studying how the distribution of a continuous response changes with a predictor. We are motivated by environmental applications in which the predictor is…
A note on the multiplicative gamma process
Daniele Durante
Adaptive dimensionality reduction in high-dimensional problems is a key topic in statistics. The multiplicative gamma process takes a relevant step in this direction, but improved…
Bayesian Learning of Dynamic Multilayer Networks
Daniele Durante, Nabanita Mukherjee, Rebecca C. Steorts
A plethora of networks is being collected in a growing number of fields, including disease transmission, international relations, social interactions, and others. As data streams c…
Bayesian inference on group differences in multivariate categorical data
Massimiliano Russo, Daniele Durante, Bruno Scarpa
Multivariate categorical data are common in many fields. We are motivated by election polls studies assessing evidence of changes in voters opinions with their candidates preferenc…
Bayesian Network--Response Regression
Lu Wang, Daniele Durante, Rex E. Jung +1
There is increasing interest in learning how human brain networks vary as a function of a continuous trait, but flexible and efficient procedures to accomplish this goal are limite…
Unifying inference on brain network variations in neurological diseases: The Alzheimer's case
Daniele Durante, Madelaine Daianu, Neda Jahanshad +2
There is growing interest in understanding how the structural interconnections among brain regions change with the occurrence of neurological diseases. Diffusion weighted MRI imagi…
Bayesian modeling of networks in complex business intelligence problems
Daniele Durante, Sally Paganin, Bruno Scarpa +1
Complex network data problems are increasingly common in many fields of application. Our motivation is drawn from strategic marketing studies monitoring customer choices of specifi…
Locally Adaptive Dynamic Networks
Daniele Durante, David B. Dunson
Our focus is on realistically modeling and forecasting dynamic networks of face-to-face contacts among individuals. Important aspects of such data that lead to problems with curren…