research
∙
01/08/2023
Skewed Bernstein-von Mises theorem and skew-modal approximations
Deterministic Gaussian approximations of intractable posterior distribut...
share
research
∙
09/24/2022
Learning and forecasting of age-specific period mortality via B-spline processes with locally-adaptive dynamic coefficients
The overarching focus on predictive accuracy in mortality forecasting ha...
research
∙
06/16/2022
Bayesian conjugacy in probit, tobit, multinomial probit and extensions: A review and new results
A broad class of models that routinely appear in several fields can be e...
research
∙
06/14/2022
Concentration and robustness of discrepancy-based ABC via Rademacher complexity
Classical implementations of approximate Bayesian computation (ABC) empl...
research
∙
09/25/2020
Bayesian Testing for Exogenous Partition Structures in Stochastic Block Models
Network data often exhibit block structures characterized by clusters of...
research
∙
09/03/2020
Scalable computation of predictive probabilities in probit models with Gaussian process priors
Predictive models for binary data are fundamental in various fields, and...
research
∙
07/16/2020
Extended Stochastic Block Models
Stochastic block models (SBM) are widely used in network science due to ...
research
∙
07/14/2020
A Class of Conjugate Priors for Multinomial Probit Models which Includes the Multivariate Normal One
Multinomial probit models are widely-implemented representations which a...
research
∙
11/15/2019
Scalable and Accurate Variational Bayes for High-Dimensional Binary Regression Models
State-of-the-art methods for Bayesian inference on regression models wit...
research
∙
11/15/2019
Asymptotically Exact Variational Bayes for High-Dimensional Binary Regression Models
State-of-the-art methods for Bayesian inference on regression models wit...
research
∙
02/19/2019
Exact Kalman Filter for Binary Time Series
Non-Gaussian state-space models arise routinely in several applications....
research
∙
02/12/2019
Bayesian cumulative shrinkage for infinite factorizations
There are a variety of Bayesian models relying on representations in whi...
research
∙
02/26/2018
Conjugate Bayes for probit regression via unified skew-normals
Regression models for dichotomous data are ubiquitous in statistics. Bes...
research
∙
11/19/2017
A note on quadratic approximations of logistic log-likelihoods
Quadratic approximations of logistic log-likelihoods are fundamental to ...
research
∙
08/07/2016
Bayesian Learning of Dynamic Multilayer Networks
A plethora of networks is being collected in a growing number of fields,...
research
∙
05/21/2015
Locally Adaptive Dynamic Networks
Our focus is on realistically modeling and forecasting dynamic networks ...
research
∙
11/19/2013
Nonparametric Bayes dynamic modeling of relational data
Symmetric binary matrices representing relations among entities are comm...
research
∙
10/07/2012
