Publications

On referred journals

[7] Camerlenghi F., Corradin R., Ongaro A.: Contaminated Gibbs–type priors. Bayesian Analysis (forthcoming) [ArXiv] [Link]

[6] Canale, A., Corradin, R., Nipoti, B.: Importance conditional sampling for Bayesian nonparametric mixtures. Statistics and Computing (2022). [ArXiv] [Link]

[5] Corradin, R., Danese, L., Ongaro, A.: Bayesian nonparametric change points detection for multivariate time series with missing observations. International Journal of Approximate Reasoning (2022). [Link]

[4] Canale, A., Corradin, R., Nipoti, B.: BNPmix: an R package for Bayesian nonparametric modelling via Pitman-Yor mixtures. Journal of Statistical Software (2021). [Link]

[3] Corradin R., Nieto Barajas L.E., Nipoti B.: Optimal stratification of survival data via Bayesian nonparametric mixtures. Econometrics and Statistics (2021). [ArXiv] [Link]

[2] Arbel, J., Corradin, R., Nipoti, B.: Dirichlet process mixtures under affine transformations of the data. Computational Statistics (2020). [ArXiv] [Link]

[1] Boscari, E., Pujolar, J.M., Dupanloup, I., Corradin, R., Congiu, L.: Captive Breeding Programs Based on Family Groups in Polyploid Sturgeons. PLoS ONE (2014). [Link]

Submitted

[2] Argiento, R., Corradin R., Guglielmi, A., Lanzarone, E.: A Bayesian nonparametric model for covariate driven clustering of blood donors data. [ArXiv]

[1] Beraha M., Corradin R.: Bayesian nonparametric model based clustering with intractable distributions: an ABC approach. [ArXiv]

Discussions and proceedings

[5] Camerlenghi F., Corradin R., Ongaro A.: On the convex combination of a Dirichlet process with a diffuse probability measure. Proceedings of 2021 Conference of the Italian Statistical Society. (2021)

[4] Corradin R., Nieto Barajas L.E., Nipoti B.: Pitman-Yor mixture models for survival data stratification. Proceedings of 2020 Conference of the Italian Statistical Society. (2020)

[3] Beraha M., Corradin R.: An ABC algorithm for random partitions arising from the Dirichlet process. Proceedings of 2020 Conference of the Italian Statistical Society. (2020)

[2] Canale, A., Corradin, R., Nipoti, B.: Galaxy color distribution estimation via dependent nonparametric mixtures. Proceedings of 2019 Conference of the Italian Statistical Society. (2019)

[1] Arbel, J., Corradin, R., Lewandowski, M.: Discussion of “Bayesian cluster analysis: Point estimation and credible balls.” by Wade and Ghahramani. Bayesian Analysis. (2018)

In preparation

[1] Camerlenghi F., Corradin R., Ongaro A.: Conditional methods for Compound random measures.

Talks and seminars

Invited presentations

Contamination of Gibbs-type prior. 2021 ISBA world meeting (June, 2021) —•— BNPmix: an new package to estimate Bayesian nonparametric mixtures. eRum (June, 2020) —•— Importance conditional sampler for Pitman-Yor and GM-DDP mixtures. Bayesian Nonparametrics for complex data (January, 2020) —•— Importance conditional sampler for nonparametric mixtures. 12th International Conference of the ERCIM WG, London (December, 2019)

Contributed presentations and posters

Model based clustering with intractable distributions. BAYSM 2021 (September, 2021, contributed) —•— On the convex combination of a Gibbs-type prior with a diffuse probability measure. 50th Scientific meeting of the Italian Statistical Society (June, 2021, contributed) —•— Approximate estimation of latent random partitions. ABC (not) in Svalbard (April, 2021, contributed) —•— BNPmix: an R package to estimate Bayesian nonparametric mixtures. EuroBioC2020 (December, 2020, contributed) —•— Importance conditional sampler for nonparametric mixture models. BNP12, University of Oxford (June, 2019, contributed) —•— Galaxy color distribution estimation via dependent nonparametric mixtures. SIS 2019 – “Smart Statistics for Smart Applications” (June, 2019, contributed) —•— Conditional Predictive Sampler for Dirichlet process mixture model. LMS Invited Lecture Series and CRISM: Summer School in Computational Statistics. University of Warwick (July, 2018, poster)

Seminars

On the contamination of Gibbs-type priors. University of Nottingham statistics seminars (April, 2021) —•— Dirichlet process mixtures for density estimation and clustering under affine transformations of the data. Department of Statistics and Quantitative Methods, University of Milano-Bicocca (December, 2017), and School of Computer Science and Statistics, Trinity College of Dublin (January, 2018), and Data Institute, Université Grenoble Alpes (March, 2018) —•— Bayesian Nonparametric Approach for Multiple Functional Data Clustering. Department of Statistics and Quantitative Methods, University of Milano-Bicocca (February, 2017)