Bayesian methods are ubiquitous in machine learning, bionformatics etc.. Nevertheless, the analysis of empirical results is typically performed by frequentist tests. This implies dealing with null hypothesis significance tests (NHST) and p-values, even though the shortcomings of such methods are well known. We are currently working on the development of nonparametric Bayesian versions of the most used standard frequentist tests (Wilcoxon signed-rank test, Wilcoxon ranksum, Friedman test, etc.) using a Dirichlet process based prior. This research can have an enormous practical impact, since these tests are commonly used in machine learning to compare algorithms performance, in medicine/pharmacology to decide the best among two treatments etc.. Moreover, recently there has been an increasing demand of alternative approaches to NHST. For instance, the journal of Basic and Applied Social Psychology, has banned the use of NHSTs and related statistical procedures from their journal, opening the doors to other approaches.

• Benavoli, Alessio, de Campos, Cassio : Bayesian Kernelised Test of (In)dependence with Mixed-type Variables. The 8th IEEE International Conference on Data Science and Advanced Analytics (DSAA). Porto, Portugal, 2021.
• Benavoli, Alessio, Corani, Giorgio, Demsar, Janez, Zaffalon, Marco: Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis. In: Journal of Machine Learning Research, vol. 18, no. 77, pp. 1-36, 2017.
• Benavoli, Alessio, de Campos, Cassio P.: Bayesian Dependence Tests for Continuous, Binary and Mixed Continuous-Binary Variables. In: Entropy, vol. 18, no. 9, pp. 1-24, 2016.
• Benavoli, Alessio, Corani, Giorgio, Mangili, Francesca: Should We Really Use Post-Hoc Tests Based on Mean-Ranks?. In: Journal of Machine Learning Research, vol. 17, no. 5, pp. 152-161, 2016.
• Benavoli, Alessio, de Campos, Cassio P.: Statistical Tests for Joint Analysis of Performance Measures. In: Suzuki, Joe; Ueno, Maomi (Ed.): Advanced Methodologies for Bayesian Networks: Second International Workshop, AMBN 2015, Yokohama, Japan, November 16-18, 2015. Proceedings, pp. 76–92, Springer International Publishing, Cham, 2015, ISBN: 978-3-319-28379-1.
• Benavoli, Alessio, Mangili, Francesca, Ruggeri, Fabrizio, Zaffalon, Marco: Imprecise Dirichlet Process With Application to the Hypothesis Test on the Probability That X ≤ Y. In: Journal of Statistical Theory and Practice, vol. 9, no. 3, pp. 658-684, 2015.
• Benavoli, Alessio, Corani, Giorgio, Mangili, Francesca, Zaffalon, Marco: A Bayesian nonparametric procedure for comparing algorithms. In: Proceedings of the 31th International Conference on Machine Learning (ICML 2015), pp. 1–9, 2015.
• Mangili, Francesca, Benavoli, Alessio, de Campos, Cassio P., Zaffalon, Marco: Reliable survival analysis based on the Đirichlet Process. In: Biometrical Journal, vol. 57, pp. 1002–1019, 2015.
• Benavoli, Alessio, Mangili, Francesca, Corani, Giorgio, Zaffalon, Marco, Ruggeri, Fabrizio: A Bayesian Wilcoxon signed-rank test based on the Đirichlet process. In: Proceedings of the 30th International Conference on Machine Learning (ICML 2014), pp. 1–9, 2014.