| Nonparametric Bayes modeling of populations of networks D Durante, DB Dunson, JT Vogelstein Journal of the American Statistical Association 112 (520), 1516-1530, 2017 | 95 | 2017 |
| Nonparametric Bayes dynamic modeling of relational data D Durante, DB Dunson Biometrika 101 (4), 883-898, 2014 | 88 | 2014 |
| Bayesian inference and testing of group differences in brain networks D Durante, DB Dunson Bayesian Analysis 13 (1), 29-58, 2018 | 75 | 2018 |
| Locally adaptive dynamic networks D Durante, DB Dunson Annals of Applied Statistics 10 (4), 2203-2232, 2016 | 26 | 2016 |
| A note on the multiplicative gamma process D Durante Statistics & Probability Letters 122, 198-204, 2017 | 20 | 2017 |
| Bayesian dynamic financial networks with time-varying predictors D Durante, DB Dunson Statistics & Probability Letters 93, 19-26, 2014 | 20 | 2014 |
| Locally adaptive factor processes for multivariate time series D Durante, B Scarpa, DB Dunson Journal of Machine Learning Research 15, 1493-1522, 2014 | 19 | 2014 |
| Conjugate Bayes for probit regression via unified skew-normal distributions D Durante Biometrika 106 (4), 765-779, 2019 | 17 | 2019 |
| Bayesian network–response regression L Wang, D Durante, RE Jung, DB Dunson Bioinformatics 33 (12), 1859-1866, 2017 | 15 | 2017 |
| Bayesian logistic Gaussian process models for dynamic networks D Durante, D Dunson AISTAT Proceedings 33, 194-201, 2014 | 14 | 2014 |
| Bayesian learning of dynamic multilayer networks D Durante, N Mukherjee, RC Steorts Journal of Machine Learning Research 18, 1-29, 2017 | 13 | 2017 |
| Bayesian cumulative shrinkage for infinite factorizations S Legramanti, D Durante, DB Dunson Biometrika 107 (3), 745-752, 2020 | 12 | 2020 |
| Tractable Bayesian density regression via logit stick-breaking priors T Rigon, D Durante Journal of Statistical Planning and Inference 211, 131-142, 2021 | 10 | 2021 |
| Conditionally conjugate mean-field variational Bayes for logistic models D Durante, T Rigon Statistical Science 34 (3), 472-485, 2019 | 9 | 2019 |
| Bayesian modelling of networks in complex business intelligence problems D Durante, S Paganin, B Scarpa, DB Dunson Journal of the Royal Statistical Society (Series C) 66 (3), 555-580, 2017 | 9 | 2017 |
| A class of conjugate priors for multinomial probit models which includes the multivariate normal one A Fasano, D Durante arXiv preprint arXiv:2007.06944, 2020 | 5 | 2020 |
| Convex mixture regression for quantitative risk assessment A Canale, D Durante, D Dunson Biometrics 74 (4), 1331-1340, 2018 | 5 | 2018 |
| Scalable and accurate variational Bayes for high-dimensional binary regression models A Fasano, D Durante, G Zanella arXiv preprint arXiv:1911.06743, 2019 | 4 | 2019 |
| Spatial modeling of brain connectivity data via latent distance models with nodes clustering E Aliverti, D Durante Statistical Analysis and Data Mining 12 (3), 185-196, 2019 | 4 | 2019 |
| A nested expectation–maximization algorithm for latent class models with covariates D Durante, A Canale, T Rigon Statistics & Probability Letters 146, 97-103, 2019 | 4 | 2019 |
David DunsonArts & Sciences Professor of Statistical Science & MathematicsVerified email at duke.edu
