Refereed journals

Bayesian Nonparametric Latent Class Analysis With Different Item Types.
Qiu, M., Paganin, S., Ohn, L., Lin, L. (2025)
Psychological Methods (accepted)
[ link | OSFpreprint | github ]

Computational methods for fast Bayesian model assessment via calibrated posterior p-values.
Paganin, S., De Valpine, P. (2024)
Journal of Computational and Graphical Statistics, 34(2), 462–473
[ link | arXiV | github | poster ]

Computational strategies and estimation performance with Bayesian semiparametric Item Response Theory models.
Paganin, S., Paciorek, C., Wehrhahn, C., A. Rodríguez, A. Rabe-Hesketh, S., De Valpine, P. (2022)
Journal of Educational and Behavioral Statistics, 35(3), 307–335
[ link | arXiv | github | poster ]

compareMCMCs: An R package for studying MCMC efficiency.
de Valpine, P., Paganin, S., Turek, D. (2022)
Journal of Open Source Software, 7(69), 3844
[ link | github | CRAN | slides ]

Centered Partition Process: Informative Priors for Clustering (with Discussion).
Paganin, S., Herring, A.H., Olshan A.F., and Dunson, D.B. (2021)
Bayesian Analysis 16(1), 301–370
[ link | arXiv | github | poster| BA Discussion Webinar ]

Bayesian modeling of networks in complex business intelligence problems.
Durante, D., Paganin, S., Scarpa, B. and Dunson, D.B. (2017)
Journal of the Royal Statistical Society: Series C 66, 555–580
[ link | poster]

Preprints

Informed Random Partition Models with Temporal Dependence
Paganin, S., Page, G. L., Quintana, F. (2023)
[ arXiV | github ]

Other publications

Refereed proceedings

Paganin, S. (2021) Semiparametric IRT models for non-normal latent traits.
CLADAG 2021 Book of Abstracts and Short Papers, pp. 178 - 181

Paganin, S., Paciorek C., de Valpine P. (2020). Bayesian IRT models in NIMBLE.
Book of Short Papers SIS 2020, pp. 644 - 649

Paganin, S. (2019). Domain knowledge based priors for clustering. Proceedings of the Conference of the Italian Statistical Society. “Smart Statistics for Smart Applications”, Pearson.

Paganin, S. (2017). Modeling of complex network data for targeted marketing. Proceedings of the Conference of the Italian Statistical Society. “Statistics and data Sciences: new challenges, new generations”, Firenze University Press.

Discussions

Aliverti, E., Paganin, S., Rigon, T. and Russo, M. (2019) A discussion on: “Latent nested nonparametric priors” by Camerlenghi, F., Dunson, D.B., Lijoi, A., Prünster, I. and Rodríguez, A. in Bayesian Analysis 14, 4, 1303–1356
[ link]

Book chapters

Aliverti E., Forastiere L., Padellini T., Paganin, S. and Wit E. (2018) Hierarchical Graphical Model for Learning Functional Network Determinants. Studies in Neural Data Science, Springer Proceedings in Mathematics & Statistics, Springer.
[ link ]

Edited Books

Argiento, R., Camerlenghi, F., Paganin, S. [Eds.] (2022) New Frontiers in Bayesian Statistics. BaYSM 2021, Online, September 1–3. Springer Proceedings in Mathematics and Statistics, Springer.
[ link ]