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Délia Boino
Submitted by dboino on 30 March 2021
Intended learning outcomes
  1. Construct models to solve concrete problems for data that have dependencies in space and/or time;
  2. Computational implementation of the several proposed modes, using appropriate software;
  3. Be able to conclude about the vulnerabilities of the model and to correct its inadequacies, to criticize the solutions and to understand how they can be improved;
  4. Understand the distinction between classical and Bayesian statistics, in order to apply them in the analysis of spatial and/or temporal data and in prediction problems.


Curricular Unit Form