Publications

Journals 

[A.36] L. Zambon, A. Agosto, P. Giudici, G. Corani, Properties of the reconciled distributions for Gaussian and count forecasts, International Journal of Forecasting, in press [doi]  [pdf]  

[A.35] L. Zambon, D. Azzimonti, G. Corani, Efficient probabilistic reconciliation of forecasts for real-valued and count time series,  Statistics and Computing, in press (2024, 34:21). [doi]  [pdf]  

[A.34] G. Corani, D. Azzimonti, N. Rubattu, Probabilistic reconciliation of count time series, Int. Journal of Forecasting, 40(2), 457-469, 2024. [doi]  [pdf] 

[A.33] Azzimonti, L., Corani, G., & Scutari, M. (2022). A Bayesian hierarchical score for structure learning from related data sets. International Journal of Approximate Reasoning, 142, 248-265 [doi] [pdf]  

[A.32] Kern, H., Corani, G., Huber, D., Vermes, N., Zaffalon, M., Varini, M. & Fringer, A. (2020). Impact on place of death in cancer patients: a causal exploration in southern Switzerland. BMC palliative care, 19(1), 1-10 [doi] [pdf] 

[A.31] Musumeci, F., Rottondi, C.E.M., Corani, G., Shahkarami, S., Cugini, F., Tornatore, M. (2019). A tutorial on machine learning for failure management in optical networks. Journal of Lightwave Technology, vol 37, issue 16 [doi] [pdf] 

[A.30] Sechidis, K., Azzimonti, L., Pocock, A., Corani, G., Weatherall, J., & Brown, G. (2019). Efficient feature selection using shrinkage estimators. Machine Learning, 108, 1261-1286 [doi] [pdf]  

[A.29] Salani, M., Corbellini, G., Corani, G. (2019). Hybrid heuristic for the optimal design of photovoltaic installations considering mismatch loss effects. Computers & Operations Research 108, pp. 112–120. [doi]

[A.28] Azzimonti, L., Corani, G., Zaffalon, M. (2019). Hierarchical estimation of parameters in Bayesian networks. Computational Statistics and Data Analysis, 137, pp. 67-91 [doi] [pdf]

[A.27] de Campos, C.P., Scanagatta, M., Corani, G., Zaffalon, M. (2018). Entropy-based pruning for learning Bayesian networks using BIC. Artificial Intelligence, 260, pp. 42–50 [doi] [pdf]

[A.26] Scanagatta, M., Corani, G., de Campos, C.P., Zaffalon, M. (2018). Approximate structure learning for large Bayesian networks. Machine Learning , 107(8-10), pp. 1209–1227 [doi] [pdf]

[A.25] Scanagatta, M., Corani, G., Zaffalon, M., Yoo, J., Kang, U. (2018). Efficient learning of bounded-treewidth Bayesian networks from complete and incomplete data sets. International Journal of Approximate Reasoning, 95, pp. 152–166 [doi] [pdf]

[A.24] Benavoli, A., Corani, G., Demšar, J., & Zaffalon, M. (2017). Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis. The Journal of Machine Learning Research, 18(1), 2653-2688.  [pdf] 

[A.23] Corani, G., Benavoli, A., Demšar, J., Mangili, F., & Zaffalon, M. (2017). Statistical comparison of classifiers through Bayesian hierarchical modelling. Machine Learning, 106, 1817-1837.  [doi]  [pdf]

[A.22]  Antonucci, A., & Corani, G. (2017). The multilabel naive credal classifier. International Journal of Approximate Reasoning, 83, 320-336. [doi] [pdf] 

[A.21]  Corani, G., & Scanagatta, M. (2016). Air pollution prediction via multi-label classification. Environmental modelling & software, 80, 259-264 [doi] [pdf]   

[A.20] Benavoli, A., Corani, G., & Mangili, F. (2016). Should we really use post-hoc tests based on mean-ranks?. The Journal of Machine Learning Research, 17(1), 152-161 [pdf]   

[A.19]  Corani, G., & Benavoli, A. (2015). A Bayesian approach for comparing cross-validated algorithms on multiple data sets. Machine Learning, 100(2-3), 285-304 [doi] [pdf]  

[A.18] Corani, G., & Mignatti, A. (2015). Robust Bayesian model averaging for the analysis of presence–absence data. Environmental and ecological statistics, 22, 513-534. [pdf]   

[A.17] Corani, G., & Mignatti, A. (2015). Credal model averaging for classification: representing prior ignorance and expert opinions. International Journal of Approximate Reasoning, 56, 264-277.  [pdf]   

[A.16] Zaffalon, M., Corani, G. (2014). Comments on "Imprecise probability models for learning multinomial distributions from data. Applications to learning credal networks" by Andrés R. Masegosa and Serafín Moral. International Journal of Approximate Reasoning 55(7), pp. 1597–1600 [pdf]   

[A.15] Corani, G., Magli, C., Giusti, A., Gianaroli, L., & Gambardella, L. M. (2013). A Bayesian network model for predicting pregnancy after in vitro fertilization. Computers in biology and medicine, 43(11), 1783-1792 [pdf]   

[A.14] Gianaroli, L., Magli, M.C., Gambardella, L., Giusti, A., Grugnetti, C., Corani, G. (2013). Objective way to support embryo transfer: a probabilistic decision. Human Reproduction 28(5), pp. 1210–1220 [pdf]

[A.13] Corani, G., Antonucci, A. (2014). Credal Ensembles of Classifiers. Computational Statistics & Data Analysis 71, pp. 818–831. [pdf]

[A.12] Zaffalon, M., Corani, G., Mauá, D.D. (2012). Evaluating credal classifiers by utility-discounted predictive accuracy. International Journal of Approximate Reasoning 53(8), pp. 1282–1301 [pdf]

[A.11] A. Giusti, P. Taddei, C. Magli, G. Corani, L. Gambardella, L. Gianaroli: "Artificial Defocus for Displaying Markers in Microscopy Z-Stacks". IEEE Transactions on Visualization and Computer Graphics, 17(12), 1757--1764, 2011. (doi). 

[A.10] Corani, G., de Campos, C.P. (2010). A tree augmented classifier based on extreme imprecise Dirichlet model. International Journal of Approximate Reasoning 51(9), pp. 1053–1068  [pdf]

[A.9] G.Corani, M. Zaffalon “JNCC2: The Java Implementation Of Naive Credal Classifier 2 ”, Journal of Machine Learning Research (Track on Open Source Software), 9, 2695--2698, 2008. [pdf]

[A.8] Corani, G., Zaffalon, M. (2008). Learning reliable classifiers from small or incomplete data sets: the naive credal classifier 2. Journal of Machine Learning Research 9, pp. 581–621. [pdf]

[A.7] G.Corani, M. Gatto “ Structural Risk Minimization: a robust method for density-dependence detection and model selection ”, Ecography, 30(3), 400–416, 2007. (pdf)

[A.6] M. Bianchi, G.Corani, G. Guariso, C. Pinto “ Prediction of ungulates abundances through local linear algorithms”, Environmental Modelling and Software, 21(10), 1508-1511, 2006 (pdf)

[A.5] G.Corani, M. Gatto “VC-Dimension and Structural Risk Minimization for the Analysis of Nonlinear Ecological Models”, Applied Mathematics and Computation, 176(1), 166-176, 2006 (pdf)

[A.4] G.Corani, M. Gatto “Model selection in demographic time series using VC-bounds”, Ecological Modelling, 191 (1), 186-195, 2006.  (pdf) >> Errata Corrige

[A.3] G.Corani, G.Guariso “Coupling fuzzy modelling and neural networks for river flood prediction”, IEEE Transactions on Men, Systems and Cybernetics, part C, 35(3), 382-391, 2005.   (pdf)

[A.2] G.Corani, G.Guariso “An application of pruning in the design of neural networks for real time flood forecasting”, Neural Computing & Applications, 14, 66-77, 2005.  (pdf)

[A.1] G.Corani “Air quality prediction in Milan: neural networks, pruned neural networks and lazy learning”, Ecological Modelling, 185, 513-529, 2005.  (pdf)

Conferences

[C.50] N. Rubattu, G. Maroni and G. Corani, Electricity Load and Peak Forecasting: Feature Engineering, Probabilistic LightGBM and Temporal Hierarchies, Proc. 8th ECML PKDD Workshop, AALTD 2023 [doi] [pdf ]

[C.49] Benavoli, A., Corani, G. (2021). State Space approximation of Gaussian Processes for time-series forecasting. In Proc. Workshop on Advanced Analytics and Learning on Temporal Data, 6th ECML PKDD Workshop, AALTD 2021 [pdf]

[C.48] Corani, G., Benavoli, A., & Zaffalon, M. (2021). Time series forecasting with Gaussian Processes needs priors. Proc. ECML PKDD 2021, Applied Data Science Track, Part IV 21 (pp. 103-117)  [pdf]

[C.47] Azzimonti, L., Corani, G., & Scutari, M. (2020, February). Structure learning from related data sets with a hierarchical bayesian score. Proc. PGM 2020 (pp. 5-16). PMLR.  [pdf]

[C.46] Corani, G., Azzimonti, D., Augusto, J. P., & Zaffalon, M. (2021). Probabilistic reconciliation of hierarchical forecast via Bayes’ rule. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2020, Proceedings, Part III (pp. 211-226)  [pdf]

[C.45]  Yoo, J., Kang, U., Scanagatta, M., Corani, G., & Zaffalon, M. (2020, January). Sampling subgraphs with guaranteed treewidth for accurate and efficient graphical inference. In Proceedings of the 13th International Conference on Web Search and Data Mining (WSDM 20) (pp. 708-716) [pdf]

[C.44] Scanagatta, M., Corani, G., Zaffalon, M. (2017). Improved local search in Bayesian networks structure learning. In Antti Hyttinen, Joe Suzuki, Brandon Malone (Eds), Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks, pp 45-56. [pdf]

[C.43] Azzimonti, L., Corani, G., Zaffalon, M. (2017). Hierarchical Multinomial-Dirichlet model for the estimation of conditional probability tables. In Raghavan, V., Aluru, S., Karypis, G., Miele, L., Wu, X. (Ed), 2017 IEEE 17th International Conference on Data Mining (ICDM), pp. 739–744. (acceptance: 20%) [pdf]

[C.42]Scanagatta, M., Corani, G., de Campos, C.P., Zaffalon, M. (2016). Learning treewidth-bounded bayesian networks with thousands of variables. In Daniel D. Lee, Masashi Sugiyama, Ulrike V. Luxburg, Isabelle Guyon, Roman Garnett (Eds), NIPS 2016: Advances in Neural Information Processing Systems 29. Acceptance rate: 23%   [pdf]

[C.41] Scanagatta, M., de Campos, C.P., Corani, G., Zaffalon, M. (2015). Learning Bayesian networks with thousands of variables. In Corinna Cortes, Neil D. Lawrence, Daniel D. Lee, Masashi Sugiyama, Roman Garnett (Eds), NIPS 2015: Advances in Neural Information Processing Systems 28. Acceptance rate: 22%   [pdf]

[C.40]  Benavoli, A., Corani, G., Mangili, F., Zaffalon, M. (2015). A Bayesian nonparametric procedure for comparing algorithms. In Francis Bach, David Blei (Eds), Proceedings of the 32th International Conference on Machine Learning (ICML 2015), pp. 1264–1272. Acceptance rate: 26%    [pdf]

[C.39] Corani, G., Benavoli, A., Mangili, F., Zaffalon, M. (2015). Bayesian hypothesis testing in machine learning. In Proc. ECML PKDD 2015 (European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases), pp. 199–202.  [pdf]

[C.38] Antonucci, A., Corani, G. (2015). The multilabel naive credal classifier. In Augustin, T., Doria, S., Miranda, E., Quaeghebeur, E. (Eds), ISIPTA '15: Proceedings of the Ninth International Symposium on Imprecise Probability: Theories and Applications, SIPTA, pp. 27–36. [pdf]

[C.37] G.  Corani, C. De Campos, A Maximum Entropy Approach to Learn Bayesian Networks from Incomplete Data, Interdisciplinary Bayesian Statistics: Springer Proceedings in Mathematics & Statistics, pp. 69--82  (doi)

[C.36] de Campos, C.P., Cuccu, M., Corani, G., Zaffalon, M. (2014). Extended tree augmented naive classifier. In van der Gaag, L., Feelders, A. (Ed), PGM'14: Proceedings of the Seventh European Workshop on Probabilistic Graphical Models, Lecture Notes in Artificial Intelligence 8754, Springer, pp. 176–189 [pdf] 

[C.35]  Corani, G., Antonucci, A., Mauá, D., Gabaglio, S. (2014). Trading off Speed and Accuracy in Multilabel Classification. In van der Gaag, L., Feelders, A. (Eds), PGM'14: Proceedings of the Seventh European Workshop on Probabilistic Graphical Models, Lecture Notes in Artificial Intelligence 8754, Springer, pp. 145–159 [pdf] 

[C.34] A. E. Rizzoli, R. Rudel, A. Forster, G. Corani, F. Cellina, L.Pampuri, R. Guidi and A. Baldassari (2014) Investigating mobility styles using smartphones: advantages and limitations according to a field study in Southern Switzerland, Proc. iEMSs 14 (7th International Congress on Environmental Modelling and Software). (pdf)    

[C.33]  Benavoli, A., Mangili, F., Corani, G., Zaffalon, M., Ruggeri, F. (2014). A Bayesian Wilcoxon signed-rank test based on the Dirichlet process. In Eric P. Xing, Tony Jebara (Eds), Proceedings of the 31st International Conference on Machine Learning (ICML 2014), pp. 1026–1034. Acceptance rate: 22% [pdf]     

[C.32]  Antonucci, A., Corani, G., Mauá, D.D., Gabaglio, S. (2013). An ensemble of Bayesian networks for multilabel classification. In Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI-13), pp. 1220–1225. Acceptance rate: 28% (pdf)    

[C.31] Corani, G., Mignatti, A. (2013). Credal model averaging of logistic regression for modeling the distribution of marmot burrows. In Cozman, F.G., Denoeux, T., Destercke, S., Seidenfeld, T. (Eds),, pp. 233–243, Proc ISIPTA '13 (the Eighth International Symposium on Imprecise Probability: Theories and Applications).  (pdf)     

[C.30] Antonucci, A., Corani, G., Gabaglio, S. (2012). Active learning by the naive credal classifier. In Cano, A., Gomez-Olmedo, M., Nielsen, T. (Eds), Proc. of the 6th European Workshop on Probabilistic Graphical Models (PGM 2012), pp. 3–10.   (pdf)     

[C.29] Corani, G., Magli, C., Giusti, A., Gianaroli, L., Gambardella, L. (2012). A Bayesian network model for predicting the outcome of in vitro fertilization. In Cano, A., Gomez-Olmedo, M., Nielsen, T. (Eds), Proc. of the 6th European Workshop on Probabilistic Graphical Models (PGM 2012), pp. 75–82. (pdf)     

[C.28] Corani, G., Antonucci, A., De Rosa, R. (2012). Compression-based AODE classifiers. In De Raedt, L. et al. (Ed), Proc. 20th European Conference on Artificial Intelligence (ECAI 2012), pp. 264–269. Acceptance rate: 28%   (pdf)     >>slides

[C.27] A. Mignatti, G. Corani, A. E. Rizzoli, "Credal Model Averaging: dealing robustly with model uncertainty on small data sets", Proc. 6th International Congress on Environmental Modelling and Software (iEMSs 2012).   (pdf)

[C.26] C. Magli, G. Corani, A. Giusti, E. Castelletti, L. Gambardella and L. Gianaroli, "A prognostic model for multiple-embryo transfers", Proc. of the Annual Meeting of the European Society on Human Reproduction and Embryology (ESHRE 2012), published in Human Reproduction (Supplement: Abstract book), vol 27, suppl 2, 2012.   (doi)

[C.25] A. Antonucci, M. Cattaneo, G. Corani, "Likelihood-Based Robust Classification with Bayesian Networks", Proc. of IPMU '12 , (14th Intern. Conf. on Information Processing and Management of Uncertainty in Knowledge-Based Systems), Communications in Computer and Information Science, 2012, Volume 299, Part 5, 491-500.   (doi)

[C.24] A. Antonucci, M. Cattaneo, G. Corani, "Likelihood-Based Naive Credal Classifier", Proc. of ISIPTA '11 , (7th Intern. Symposium on Imprecise Probability: Theories and Applications), pages 21--30.   (pdf)

[C.23] Marco Zaffalon, Giorgio Corani, Denis Mauá, "Utility-Based Accuracy Measures to Empirically Evaluate Credal Classifiers", Proc. of ISIPTA '11, (7th Intern. Symposium on Imprecise Probability: Theories and Applications), pages 401--410.   (pdf)

[C.22]  A. Giusti, C. Magli, G. Corani, L. Gambardella, L. Gianaroli, “Observing the 3-Dimensional Morphology of 4-Cell Embryos using Computer Analysis of Image Z-Stacks”, Proceedings of the Annual Meeting of the European Society on Human Reproduction and Embryology (ESHRE 2011), published in Human Reproduction (Supplement: Abstract book), vol.26, 2011.

[C.21] A. Giusti, G. Corani, L. Gambardella, C. Magli and L. Gianaroli, "3D Localization of Pronuclei of Human Zygotes Using Textures from Multiple Focal Planes", Proc. of the Medical Image Computing and Computer-Assisted Intervention (MICCAI 2010), Lecture Notes in Computer Science, 2010, Volume 6362/2010, pages 488-495. (acceptance rate: 32%).   (pdf)

[C.20] G. Corani, A. Giusti, D. Migliore, and J. Schmidhuber, "Robust Texture Recognition Using Credal Classifiers.", Proc. of the British Machine Vision Conference 2010 (BMVC 2010), pages 78.1-78.10. (acceptance rate: 34%).   (pdf)

[C.19] G. Corani, A. Benavoli, "Restricting the IDM for classification: Good and evil of epsilon", Int. Conf. on Information Processing and Management of Uncertainty in Knowledge-Based Systems, (IPMU 2010), Part 1, pages 328--337.   (pdf) 

[C.18] A. Giusti, G. Corani, L. Gambardella, C. Magli and L. Gianaroli, "Blastomere Segmentation and 3D Morphology Measurements of Early Embryos from Hoffman Modulation Contrast Image Stacks", IEEE International Symposium on Biomedical Imaging (ISBI) 2010. 

[C.17] A. Giusti, G. Corani, L. Gambardella, C. Magli and L. Gianaroli “Lighting-Aware Segmentation of Microscopy Images for In Vitro Fertilization”, Proc. of International Symposium on Visual Computing (ISVC) 2009, Las Vegas, Springer LNCS vol. 5875/2009, 576--585. 

[C.16]  A. Giusti, G. Corani, L. Gambardella, C. Magli and L. Gianaroli “Segmentation of Human Zygotes in Hoffman Modulation Contrast Images”, Proc. of Medical Image Understanding and Analysis (MIUA), 2009, 189--193     [pdf]

[C.15] G. Corani, M. Zaffalon, “Lazy Naive Credal Classifier”, 1st ACM SIGKDD workshop on Knowledge Discovery from Uncertain Data (KDD 09), 30- -37, Paris, 2009     [pdf]   >>slides    

[C.14] G. Corani, C. De Campos, S. Yi, “A Tree Augmented Classifier Based on Extreme Imprecise Dirichlet Model”, Proc. 6th International Symposium on Imprecise Probability: Theories and Applications (ISIPTA 09), 89--98, Durham, 2009    [pdf]   >> slides

[C.13] G.Corani, A. Rizzoli, A. Salvetti, M. Zaffalon “Reproducing human decisions in reservoir management: the case of lake Lugano”, Proc. of the 4th International ICSC Symposium on Information Technologies in Environmental Engineering, Thessaloniki, pages 252--263, 2009     [pdf]

[C.12] G.Corani, M. Zaffalon “Credal Model Averaging: an Extension of Bayesian Model Averaging to Imprecise Probabilities, Proc. ECML PKDD '08 (European Conf. on Machine Learning and Principles and Practice of Knowledge Discovery in Databases), pages 257--271 (acceptance rate: 20%)    [pdf]     >> slides     

[C.11] G.Corani, M. Zaffalon “Naive Credal Classifier 2: an Extension of Naive Bayes for Delivering Robust Classifications”, Proc. DMIN'08 (Int. Conf. on Data Mining), Las Vegas, July 2008     [pdf]     >>slides

[C.10] M. Bianchi, G.Corani, G. Guariso “PM10 forecasting with a local linear approachs”, Proc. Advanced Atmospheric Aerosol Symposium, Milano, 215-221, 2006    [pdf]

[C.9] Giorgio Corani, Chris Edgar, Isabelle Marshall, Keith Wesnes and Marco Zaffalon “Classification of dementia types from cognitive profiles data”, Proc. ECML PKDD '06 (European Conf. on Machine Learning and Principles and Practice of Knowledge Discovery in Databases), pages 470--477 (acceptance rate: 25%) [pdf]

[C.8] G.Corani, M. Gatto “An application of Structural Risk Minimization to the selection of ecological models”, 16th IFAC World Conference, Prague, July 2005

[C.7]  G.. Corani, G.Guariso, “Fuzzy modelling of basin state and Neural Networks for flood forecasting”, 2nd International Environmental Modelling and Software Society Conference, Osnabruck, June 2004. [pdf]

[C.6] M. Cecchetti, G. Corani, G. Guariso, “Artificial Neural Networks Prediction of PM10 in the Milan area”, 2nd International Environmental Modelling and Software Society Conference, Osnabruck, June 2004.  [pdf]

[C.5] S. Barazzetta, G. Corani, “First results on the prediction of PM10 in Milan: the Air Sentinel project”, 9th  International Conference on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes, Garmish, May 2004.  

[C.4]  L.Bolognini, G. Corani, C. Faggioni, G. Guariso, “Neural networks forecast of ozone pollution in Milan”, 8th Int. Conference on Engineering Applications of Neural Networks, Malaga, September 2003. 

[C.3] S. Castelli, G. Corani, G. Guariso, “Structural identification of multivariate neural networks for rainfall runoff modeling”, 13th  IFAC Symposium on System Identification, Rotterdam, August 2003

[C.2]  S. Barazzetta, G.Corani, G. Guariso, “A Neural Emission-Receptor Model for Ozone Reduction Planning”, 1st International Environmental Modelling and Software Society Conference, Lugano, 2002.  This paper has been awarded with the “Best Paper Prize” of the Conference. [pdf]

[C.1] A.Castelletti, G.Corani, E.Weber, A. Rizzoli, R.Soncini Sessa: “A reinforcement learning approach for the operational management of a water system” IFAC Workshop on Modeling and Control in Environmental Issues. Yokohama, August 2001.   [pdf]

Book chapters

[B.3]   Corani, G., Abellán,J. , Masegosa, A., Moral,S., Zaffalon,M. “Classification”, chapter within the book Introduction to Imprecise Probabilities, p. 261-285 editors: Augustin,T., Coolen,F., de Cooman,G., Troffaes,M. (2014) [pdf]

[B.2]  G.Corani, A. Antonucci, M. Zaffalon, “Bayesian Networks with Imprecise Probabilities: Theory and Application to Classification”, Ch. 4 of the book Data Mining: Foundations and Intelligent Paradigms , editors: D.E. Holmes, L.C. Jain, pages 49--93, 2012 [pdf]

[B.1]   R. Bellasio, R. Bianconi, G. Corani, G. Maffeis, M. Molari and N. Quaranta, “SINERGIE, A Decision Support System for Environmental Emergencies Management”, in "Environmental Sciences and Environmental Computing - Volume II”.  E-book edited by “The Envirocomp Institute”. 2005

Invited talks and tutorials

[T.7] G. Corani, J. Demsar and A. Benavoli Comparing competing algorithms: Bayesian versus frequentist hypothesis testing, ECML/PKDD 2016 (slides and code).

[T.6] G. Corani, 10 years of credal classification , Sixth Workshop on Principles and Methods of Statistical Inference with Interval Probability (WPMSIIP 2013).

[T.5] A. Antonucci, G. Corani and D. Maua, Bayesian networks with imprecise probabilities: theory and applications to knowledge-based systems and classification, IJCAI '13 (23rd Int. Joint Conf. on Artificial Intelligence, 2013. 

[T.4] A. Antonucci, C.P. De Campos and G. Corani, “Classification with Imprecise Probabilities”, 4th SIPTA Summer School , Durham, September 2010. >> All slides of the school. 

[T.3] A. Antonucci, G. Corani, “ “Bayesian Networks with Imprecise Probabilities: Theory and Applications to Knowledge-based Systems and Classification”,”: “AAAI-10: Twenty-Fourth Conference on Artificial Intelligence”, Atlanta, July 2010. 

[T.2] G. Corani, “Statistical approaches for air pollution prediction”, within the workshop: “Méthodes Statistiques et Pollution”, Insa de Rouen, June 2008    >> slides  

[T.1] G. Corani, “Naive Credal Classifier”, within the “SIPTA (Society for Imprecise Probability: Theories and Applications) School 2008",, (15 mins presentation, within the more general tutorial about credal networks), Montpellier, July 2008    >> slides