Checking date: 19/05/2022

Course: 2022/2023

Stochastic Dynamical Systems
Study: Bachelor in Data Science and Engineering (350)

Coordinating teacher: MEILAN VILA, ANDREA

Department assigned to the subject: Statistics Department

Type: Electives
ECTS Credits: 6.0 ECTS


Requirements (Subjects that are assumed to be known)
Probability and Data Analysis
Skills and learning outcomes
Description of contents: programme
1. Introduction to Stochastic Processes 2. Discrete Markov Chains 3. Continuous time Markov Chains 4. Renewal Processes 5. Queuing theory 6. Random Graphs 7. Case studies: Monte Carlo Algorithm, PageRank Algorithm, Call centers, Social networks.
Learning activities and methodology
Theory (4 ECTS). Theory classes with additional material available on the Web. Practical classes (2 ECTS) Problem solving classes. Problem based learning classes.
Assessment System
  • % end-of-term-examination 60
  • % of continuous assessment (assigments, laboratory, practicals...) 40
Calendar of Continuous assessment
Basic Bibliography
  • R. Durrett. Essentials of stochastic processes. Springer. 2012 (2nd ed.)
Recursos electrónicosElectronic Resources *
Additional Bibliography
  • S.M. Ross. Stochastic Processes. John Wiley & Sons, inc.. 1996 (2nd. ed.)
(*) Access to some electronic resources may be restricted to members of the university community and require validation through Campus Global. If you try to connect from outside of the University you will need to set up a VPN

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