Preprints

  • D’Angelo L., Nipoti B., and Ongaro A. (2025) “Dependent Dirichlet processes via thinning”. arXiv preprint, arXiv:2506.18223, 1–28.

  • D’Angelo L., Denti F., Canale A., and Guindani M. (2025) “Decoding neuronal ensembles from spatially-referenced calcium traces: a Bayesian semiparametric approach”. arXiv preprint, arXiv:2508.09576, 1–17.

  • Denti F. and D’Angelo L. (2025) “sanba: Fitting shared atoms nested models via MCMC or variational Bayes”. arXiv preprint, arXiv:2508.09758, 1–29.



Articles in refereed journals

  • Denti F. and D’Angelo L. (2025) “The generalized nested common atoms model”. Econometrics and Statistics, 1–28 (in press). DOI: 10.1016/j.ecosta.2025.01.001.
    Article - Citation

  • D’Angelo L. and Denti F. (2024) “A finite-infinite shared atoms nested model for the Bayesian analysis of large grouped data”. Bayesian Analysis, 1–34 (in press). DOI: 10.1214/24-BA1458.
    Article - Citation

  • D’Angelo L. and Canale A. (2023) “Efficient posterior sampling for Bayesian Poisson regression”. Journal of Computational and Graphical Statistics, 32(3), 917–926. DOI: 10.1080/10618600.2022.2123337.
    Article - Citation

  • D’Angelo L., Canale A., Yu Z., and Guindani M. (2023) “Bayesian nonparametric analysis for the detection of spikes in noisy calcium imaging data”. Biometrics, 79(2), 1370–1382. DOI: 10.1111/biom.13626.
    Article - Citation



Ph.D. Thesis

  • D’Angelo L. (2022) “Bayesian modeling of calcium imaging data” (PhD Thesis).
    Supervisor: Prof. Antonio Canale; Co-supervisor: Prof. Michele Guindani.
    Research Padua Archive - Full text - Citation


Conference proceedings and discussions

  • D’Angelo L., Nipoti B., and Ongaro A. (2025) “Modeling related survival samples via dependent nonparametric mixtures”, in Statistics for Innovation III, Conference proceedings of SIS 2025, Springer, 73–78.

  • D’Angelo L., Nipoti B., and Ongaro A. (2024) “Two-level clustering of patients and hospitals via thinned dependent Dirichlet process mixtures”, in Methodological and Applied Statistics and Demography II, Conference proceedings of SIS 2024, Springer, 37–42.

  • D’Angelo L. (2025) “Exploring the challenges of the analysis of the Allen Brain Observatory dataset”, in Advances in Neural Data Science, Proceedings of the Data Research Camp 2022, Venice, Italy; Springer, 1–11.

  • D’Angelo L. (2023) “A comparison of computational approaches for posterior inference in Bayesian Poisson regression”, in Book of Short Papers SIS 2023, 903–907.

  • D’Angelo L. and Denti, F. (2023) “Bayesian analysis of Amazon’s best-selling books via finite nested mixture models”, in Book of Short Papers SIS 2023, 1117–1120.

  • D’Angelo L. (2022) “Bayesian nonparametric clustering of spatially-referenced spike train data”, in Book of Short Papers SIS 2022, 514–519.

  • Denti F., D’Angelo L., and Guindani M. (2022) “Bayesian approaches for capturing the heterogeneity of neuroimaging experiments”, in Book of Short Papers SIS 2022, 18–29.

  • D’Angelo L. and Canale A. (2021) Contributed Discussion on: “Centered Partition Processes: Informative Priors for Clustering”, in Bayesian Analysis, 16(1), 356–358.

  • D’Angelo L., Canale A., Yu Z., and Guindani M. (2021) “Detection of neural activity in calcium imaging data via Bayesian mixture models”, in Book of Short Papers SIS 2021, 745–750.

  • D’Angelo L. (2019) “Model based clustering in group life insurance via Bayesian nonparametric mixtures”, in Book of Short Papers SIS 2019.


Software

  • Denti F. and D’Angelo L. (2025) “sanba: Fitting Shared Atoms Nested Models via MCMC or Variational Bayes”, R package.
    CRAN - Github

  • D’Angelo L. and Denti F. (2023) “SANple: Fitting Shared Atoms Nested Models via Markov Chains Monte Carlo”, R package.
    CRAN - Github

  • D’Angelo L. (2021) “bpr: Fitting Bayesian Poisson Regression”, R package.
    CRAN - Github