Degree | Type | Year | Semester |
---|---|---|---|
2503852 Applied Statistics | OB | 3 | 2 |
It is recommended that you have passed the Calculus, Probability and Inference subjects. A minimum knowledge of Excel and R software is needed.
Knowing tools to evaluate and quantify risk: theory of extreme values and Bayesian networks.
The subject is structured in two parts:
Topic-1: Evaluation of risk with Bayesian networks.
Introduction. From the Bayes Formula to the Bayesian Networks.
Inference with Bayesian networks.
Parameter and structure learning.
Bayesian classifiers as a tool for risk assessment.
Topic 2: Complex Systems and extreme values.
Introduction to Statistical Modeling.
Complex Systems.
Distributions on a threshold. Selection and diagnosis.
Classical theory of extreme values.
Unless the requirements enforced by the health authorities demand a prioritization or reduction of these contents.
The subject is structured from theoretical classes, problems and practices. The follow-up of the subject must be present, but it will be necessary to extend the teacher's explanations with the student's autonomous study, with the support of the reference bibliography.
The class of problems will be devoted to the resolution oriented to some problems proposed. Students' participation in the problem classes will be especially valued. Practical classes will introduce Excel and R software tools with statistical applications. You will have to deliver some practical work.
The proposed teaching methodology may experience some modifications depending on the restrictions to face-to-face activities enforced by health authorities.
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Title | Hours | ECTS | Learning Outcomes |
---|---|---|---|
Type: Directed | |||
Practices (deliveries, controls) | 12 | 0.48 | 1, 3, 9, 8 |
Problems | 14 | 0.56 | 2, 4, 14, 5, 6, 13, 15 |
Theory | 26 | 1.04 | 2, 1, 10, 3, 9, 4, 14, 5, 6, 8, 13, 11, 12, 7, 15 |
Type: Supervised | |||
Tutorials | 10 | 0.4 | 10, 3, 11, 12, 7 |
Type: Autonomous | |||
Practical work with computer tools | 30 | 1.2 | 1, 3, 9, 8, 15 |
Study and think problems | 40 | 1.6 | 4, 14, 5, 6, 13, 15 |
The final qualification of this subject is obtained as the average of the qualifications of the two parts of the syllabus (exposed in the Contents). The parties will be assessed with deliveries of exercises, controls of problems, practices, and exams. The examinations can only be recovered together at the end, as long as the student has previously achieved a 3.5 in each partial. Work in general is not recovered.
Student’s assessment may experience some modifications depending on the restrictions to face-to-face activities enforced by health authorities.
Title | Weighting | Hours | ECTS | Learning Outcomes |
---|---|---|---|---|
Deliveries and controls Unit-1 | 17% | 7 | 0.28 | 2, 1, 10, 3, 9, 4, 14, 5, 6, 8, 13, 11, 12, 7, 15 |
Deliveries and controls Unit-2 | 17% | 7 | 0.28 | 2, 1, 10, 3, 9, 4, 14, 5, 6, 8, 13, 11, 12, 7, 15 |
Final exam Theme-1 | 33% | 2 | 0.08 | 2, 1, 10, 3, 9, 4, 14, 5, 6, 8, 13, 11, 12, 7, 15 |
Final exam Theme-2 | 33% | 2 | 0.08 | 2, 1, 10, 3, 9, 4, 14, 5, 6, 8, 13, 11, 12, 7, 15 |
Excel and R.