Checking date: 17/05/2022


Course: 2022/2023

Bayesian Data Analysis
(16502)
Study: Dual Bachelor in Data Science and Engineering and Telecommunication Technologies Engineering (371)


Coordinating teacher: AUSIN OLIVERA, MARIA CONCEPCION

Department assigned to the subject: Department of Statistics

Type: Compulsory
ECTS Credits: 6.0 ECTS

Course:
Semester:




Requirements (Subjects that are assumed to be known)
Probability and Data Analysis Introduction to Statistical Modeling Statistical Learning
Objectives
CB2: Students should know how to apply their knowledge in their jobs or vocation in a profesional manner and have the skills that are usually demonstrated through the elaboration and defense of arguments and the resolution of problems within their area of study. CB3: Students should have the ability to gather and interpret relevant data (ussually within their area of study) to make judgments that include a reflection on relevant issues of social, scientific or ethical nature CE2: Ability to correctly identify problems of predictive nature corresponding to certain objectives and data and to use the basic results of regression analysis as a basic basis for prediction methods. CG1: Adequate knowledge and skills to analyze and synthesize basic problems related to engineering and data science, solve them and communicate them efficiently. CG4: Ability to solve technological, computer, mathematical and statistical problems that may arise in engineering and data science.
Skills and learning outcomes
Description of contents: programme
0. Review of Probability and random variables 1. Frequentist vs. Bayesian approaches 2. Conjugate distributions 3. Gaussian models 4. Sampling-Based Approaches to Bayesian Estimation 5. Regression and Linear Models 6. Generalized linear models 7. Bayesian clustering 8. Non linear regression
Learning activities and methodology
AF1: THEORETICAL-PRACTICAL LESSONS where the knowledge that students should acquire is presented. Students will receive class notes and basic reference texts to facilitate the follow-up of the classes and the development of the subsequent work. Exercises, practical problems will be solved by students and workshops and evaluation tests will be held to acquire the necessary skills. AF3: INDIVIDUAL OR GROUP WORK OF THE STUDENT. AF9: FINAL EXAM where the knowledge, skills and abilities acquired throughout the course will be assessed globally. MD1: CLASS THEORY. Presentations offered by the teacher in class with computer support and audiovisual media, where the main concepts of the subject are developed and materials and bibliography are provided to complement the students' learning. MD2: PRACTICES. Resolution of practical case studies, problems, etc. proposed by the teacher individually or in groups. MD3: TUTORIALS. Individualized assistance (individual tutorials) or group (collective tutorials) offered to students by the teacher.
Assessment System
  • % end-of-term-examination 60
  • % of continuous assessment (assigments, laboratory, practicals...) 40
Calendar of Continuous assessment
Basic Bibliography
  • Albert, J.. Bayesian Computation with R. Springer. . 2007
  • Kruschke, J.K.,. Doing Bayesian Data Analysis: A Tutorial with R and BUGS. Academic Press.. 2015
  • Robert, C.P., Casella, G. Introducing Monte Carlo Methods with R. Springer . 2010

The course syllabus may change due academic events or other reasons.