Special Topics: Generalized Markovian Systems
| Title | ΕΙΔΙΚΑ ΘΕΜΑΤΑ ΙΙ (ΣΜ25): Γενικευμένα Μαρκοβιανά Συστήματα / Special Topics: Generalized Markovian Systems |
| Code | 0762 |
| Faculty | Sciences |
| School | Mathematics |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Winter |
| Coordinator | Georgios Tsaklidis |
| Common | No |
| Status | Active |
| Course ID | 600019977 |
Programme of Study: PMS Tmīmatos Mathīmatikṓn (2018-sīmera)
Registered students: 11
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| STATISTIKĪ KAI MONTELOPOIĪSĪ | Compulsory Course | 1 | 1 | 10 |
| Academic Year | 2025 – 2026 |
| Class Period | Winter |
| Instructors from Other Categories |
|
| Weekly Hours | 3 |
| Total Hours | 39 |
| Class ID | 600290410
|
Type Of Offer
- Disciplinary Course
Course Type 2021
Specialization / Direction
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600290410
- eLearning: https://elearning.auth.gr/course/view.php?id=8310
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
- Greek (Instruction, Examination)
Prerequisites
General Prerequisites
General background on Probability Theory and Statistics
Learning Outcomes
Upon successful completion of the course the students will have knowledge on basic issues of Homogeneuous Markov Systems (HMSs) in discrete and continuous time, of Queueing Systems, Hidden Markov Systems and Semi-Markov Reward Models.
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Work autonomously
- Work in teams
- Generate new research ideas
- Advance free, creative and causative thinking
Course Content (Syllabus)
1. Βasic issues on Homogeneuous Markov Systems (HMSs) in discrete and continuous time,
2. Basic Queueing Systems
3. Introduction in Hidden Markov Systems theory.
Keywords
Homogeneuous Markov Systems (HMSs), Queueing Systems, Hidden Markov Systems, Semi-markov reward Models.
Educational Material Types
- Notes
- Multimedia
- Book
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
Description
Use of computer programming to solve exercises
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 39 | 1.3 | |||
| Reading Assigment | 196 | 6.5 | |||
| Written assigments | 60 | 2 | |||
| Exams | 5 | 0.2 | |||
| Total | 300 | 10 |
Student Assessment
Description
The final mark consists by 85% of the exams grade and by 15% of the exercises grade.
Student Assessment methods
- Written Exam with Extended Answer Questions (Formative, Summative)
- Written Assignment (Formative, Summative)
- Written Exam with Problem Solving (Formative, Summative)
Bibliography
Course Bibliography (Eudoxus)
Π. Βασιλείου, Μαθηματικές Μέθοδοι στις Επιχειρησιακές Έρευνες
Additional bibliography for study
1. Papers on HMSs and Semi-Markov Reward Models
2. Π. Βασιλείου, Μαθηματικές Μέθοδοι στις Επιχειρησιακές Έρευνες
3. Rabiner & Juang, 1986. An Introduction to the Hidden Markov Model
Last Update
12-05-2025
