Articles in refereed journals

  1. 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, Series A 182(1), 225–247. [Link] [GitHub Repository].
  2. Durante, D., Canale, A. and Rigon, T. (2019). A nested expectation-maximization algorithm for latent class models with covariates, Statistics and Probability Letters 146, 97–103. [Link] [GitHub Repository].

Refereed conference proceedings, publications in monographs

  1. Rigon, T. (2018). Logit stick-breaking priors for partially exchangeable count data. In Book of Short Papers SIS 2018 (Abbruzzo, A., Piacentino, D., Chiodi, M., and Brentari, E., editors). ISBN: 9788891910233. [Link].
  2. Caponera, A., Denti, F., Rigon, T., Sottosanti, A. and Gelfand, A. (2018). Hierarchical Spatio-Temporal Modeling of Resting State fMRI Data. In Studies in Neural Data Science (Canale, A., Durante, D., Paci, L., Scarpa, B., editors). [Link] [GitHub Repository].

Submitted and working papers

  1. Durante, D. and Rigon, T. (2019+). Conditionally conjugate mean-field variational Bayes for logistic models, arXiv:1711.06999. Submitted. [ArXiv] [GitHub Repository].

  2. Rigon, T. and Durante, D., (2019+), Tractable Bayesian density regression via logit stick-breaking priors, arXiv:1701.02969. Submitted. [ArXiv] [GitHub Repository].

  3. Lijoi, A., Prünster, I. and Rigon, T. (2019+). Sampling hierarchies of discrete random structures. Submitted.

  4. Lijoi, A., Prünster, I. and Rigon, T. (2019+). Bayesian inference for finite-dimensional discrete priors. In preparation.

  5. Rigon, T., Scarpa, B. and Barbierato, G. (2019+). An enriched functional mixture model for flight route segmentation. In preparation.