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(objectives)
• Bayesian networks. Structural and parameter learning. Estimation of association between variables.
• Introduction to causality.
• Sampling techniques.
• Quality analysis of the information in big data. Weighting, calibrating and sampling techniques for going from big data to smart data.
Prerequisites: students need to have passed the two previous Statistics courses.
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Università Roma Tre