Articles in refereed journals

  1. Durante, D. and Rigon, T. (2019+). Conditionally conjugate mean-field variational Bayes for logistic models, Statistical Science. In press. [ArXiv] [GitHub Repository].

  2. 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].
  3. 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].

Submitted and working papers

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

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

  3. Lijoi, A., Prünster, I. and Rigon, T. (2019+). The Pitman–Yor multinomial process for mixture modeling. Submitted.

  4. Rigon, T. (2019+). An enriched mixture model for functional clustering. Submitted. [ArXiv].

  5. Lijoi, A., Prünster, I. and Rigon, T. (2019+). Finite-dimensional discrete random structures and Bayesian clustering. Submitted.

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].