Fasano, A., Rebaudo, G., Durante, D. and Petrone, S. [2021]. A closedform filter for binary time series. Statistics and Computing. [in press]. 
Legramanti, S., Rigon, T. and Durante, D. [2021]. Bayesian testing for exogenous equivalence structures in stochastic blockâ€“models. Sankhya A. [in press]. 
Rigon, T. and Durante, D. [2021]. Tractable Bayesian density regression via logit stickbreaking priors. Journal of Statistical Planning and Inference. 211, 131–142. 
Legramanti, S., Durante, D. and Dunson, D.B. [2020]. Bayesian cumulative shrinkage for infinite factorizations. Biometrika, 107, 745–752. 
Durante, D. and Guindani, M. [2020]. Bayesian methods in brain networks. Wiley StatsRefStatistics Reference Online, 1–10. 
Durante, D. [2019]. Conjugate Bayes for probit regression via unified skewnormal distributions. Biometrika, 106, 765–779. 
Durante, D. and Rigon, T. [2019]. Conditionally conjugate meanfield variational Bayes for logistic models. Statistical Science, 34, 472–485. 
Durante, D., Canale, A. and Rigon, T. [2019]. A nested expectation–maximization algorithm for latent class models with covariates. Statistics & Probability Letters, 146, 97–103. 
Rigon, T., Durante, D. and Torelli, N. [2019]. Bayesian semiparametric modelling of contraceptive behavior in India via sequential logistic regressions. Journal of the Royal Statistical Society: A, 182, 225–247. 
Canale, A., Durante, D. and Dunson, D. B. [2018]. Convex mixture regression for quantitative risk assessment. Biometrics, 74, 1331–1340. 
Russo, M., Durante, D. and Scarpa, B. [2018]. Bayesian inference on group differences in multivariate categorical data. Computational Statistics & Data Analysis, 126, 136–149. 
Canale, A., Durante, D., Paci, L. and Scarpa, B. [2018]. Connecting statistical brains. Significance, 15, 38–40.

Durante, D. and Dunson, D. B. [2018]. Bayesian inference and testing of group differences in brain networks. Bayesian Analysis, 13, 29–58.

Durante, D., Dunson, D. B. and Vogelstein, J. T. [2017]. Nonparametric Bayes modeling of populations of networks. Journal of the American Statistical Association, 112, 1516–1530 [with discussion]. 
Durante, D., Mukherjee, N. and Steorts, R. C. [2017]. Bayesian learning of dynamic multilayer networks. Journal of Machine Learning Research, 18, 1–29. 
Wang, L., Durante, D., Jung, R. E. and Dunson, D. B. [2017]. Bayesian networkresponse regression. Bioinformatics, 33, 1859–1866. 
Durante, D. [2017]. A note on the multiplicative gamma process. Statistics & Probability Letters, 122, 198–204.

Durante, D., Paganin, S., Scarpa, B. and Dunson, D. B. [2017]. Bayesian modelling of networks in complex business intelligence problems. Journal of the Royal Statistical Society: C, 66, 555–580. 
Durante, D. and Dunson, D. B. [2016]. Locally adaptive dynamic networks. Annals of Applied Statistics. 10, 2203–2232.

Durante, D. and Dunson, D. B. [2014]. Nonparametric Bayes dynamic modelling of relational data. Biometrika, 101, 883–898. 
Durante, D. and Dunson, D. B. [2014]. Bayesian dynamic financial networks with timevarying predictors. Statistics & Probability Letters, 93, 19–26. 
Durante, D., Scarpa, B. and Dunson, D. B. [2014]. Locally adaptive factor processes for multivariate time series. Journal of Machine Learning Research, 15, 1493–1522. 