Intended learning outcomes
- Construct models to solve concrete problems for data that have dependencies in space and/or time;
- Computational implementation of the several proposed modes, using appropriate software;
- 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;
- 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.