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Course: 2022/2023

Stochastic Dynamical Systems

(17313)

Study: Bachelor in Data Science and Engineering (350)

Coordinating teacher: MEILAN VILA, ANDREA

Department assigned to the subject: Department of Statistics

Type: Electives

ECTS Credits: 6.0 ECTS

Course: 4º

Semester:

Requirements (Subjects that are assumed to be known)

Probability and Data Analysis

Skills and learning outcomes

Link to document

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

Final exam (60% of the final grade). Partial exams, problem lessons and homeworks (40% of the final grade).

% 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.)

Electronic Resources *

R. Durrett · Essentials of Stochastic Processes : http://www.math.duke.edu/~rtd/EOSP/EOSP2E.pdf

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.