EMÜ722 - ADVANCED STOCHASTIC PROCESSES
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| ADVANCED STOCHASTIC PROCESSES | EMÜ722 | Any Semester/Year | 3 | 0 | 3 | 10 |
| Prequisites | ||||||
| Course language | Turkish | |||||
| Course type | Elective | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Question and Answer Problem Solving Project Design/Management Other: Individual Study | |||||
| Instructor (s) | To be determined by the program board | |||||
| Course objective | The objective of this course is to develop students? skills and competency to model stochastic processes in real systems and implement and synthesize methods to analyze the random characteristics of these systems | |||||
| Learning outcomes |
| |||||
| Course Content | Poisson processes Markov Chains Renewal Processes Martingales Random walks and Brownian motion Parameter estimation in stochastic processes | |||||
| References | Ross, M.S. (1996) Stochastic Processes, 2nd ed., John Wiley and Sons. Gallager, R.G. (2013) Stochastic Processes: Theory for Applications, 2nd ed., Cambridge University Press. Karlin, S. and Taylor, H.M (1998) A First Course in Stochastic Processes, 2nd Ed., Academic Press. Karlin, S. and Taylor, H.M (1981) A Second Course in Stochastic Processes, 1st Ed., Academic Press. Tijms, H. C. (2003) A First Course in Stochastic Models, Wiley. Up-to-date research and application articles about stochastic processes | |||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | Elements and classification of stochastic processes |
| Week 2 | Poisson Process |
| Week 3 | Discrete-time Markov Chains |
| Week 4 | Continuous-time Markov Chains |
| Week 5 | Renewal Processes |
| Week 6 | Compounding Stochastic Processes |
| Week 7 | Midterm exam |
| Week 8 | Martingales |
| Week 9 | Martingales |
| Week 10 | Random Walks |
| Week 11 | Brownian Motion |
| Week 12 | Brownian Motion |
| Week 13 | Parameter estimation in stochastic processes |
| Week 14 | Parameter estimation in stochastic processes |
| Week 15 | Study for the Final Exam |
| Week 16 | Final Exam |
Assesment methods
| Course activities | Number | Percentage |
|---|---|---|
| Attendance | 0 | 0 |
| Laboratory | 0 | 0 |
| Application | 0 | 0 |
| Field activities | 0 | 0 |
| Specific practical training | 0 | 0 |
| Assignments | 5 | 15 |
| Presentation | 0 | 0 |
| Project | 1 | 10 |
| Seminar | 0 | 0 |
| Midterms | 1 | 25 |
| Final exam | 1 | 50 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 7 | 50 |
| Percentage of final exam contributing grade succes | 1 | 50 |
| Total | 100 | |
WORKLOAD AND ECTS CALCULATION
| Activities | Number | Duration (hour) | Total Work Load |
|---|---|---|---|
| Course Duration (x14) | 14 | 3 | 42 |
| Laboratory | 0 | 0 | 0 |
| Application | 0 | 0 | 0 |
| Specific practical training | 0 | 0 | 0 |
| Field activities | 0 | 0 | 0 |
| Study Hours Out of Class (Preliminary work, reinforcement, ect) | 13 | 6 | 78 |
| Presentation / Seminar Preparation | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Homework assignment | 6 | 15 | 90 |
| Midterms (Study duration) | 1 | 35 | 35 |
| Final Exam (Study duration) | 1 | 45 | 45 |
| Total Workload | 35 | 104 | 290 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition level* | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1. Reach the necessary knowledge and methods required within the scope of industrial engineering through scientific research. Utilize these knowledge and methods upon evaluation and synthesis and implement them | X | ||||
| 2. Follow the innovations, developments and literature on an international basis in the field of industrial engineering; have the competency to convert the research activities into scientific national and international publications and to contribute to the national and international science and technology literature. | X | ||||
| 3. Perform a comprehesive analysis of the decision making problems; with a critical view evaluate the operations research and data based methodologies to model and solve these problems; implement after the synthesis or the development of these methods. | X | ||||
| 4. Perceive independently, design, plan, manage, monitor and conclude the research and development study process in the field of industrial engineering. | X | ||||
| 5. Are aware of the academic responsbilities; describe the scientific, technological, economic, social, environmental and cultural impacts of the applications of Industrial Engineering; based on necessity, work individually or as a team member taking the scientific and institutional ethical values. | X | ||||
| 6. Evaluate critically, report and present the results of the advanced research stuies and projects carried out in the field of industrial engineering | X | ||||
| 7. Have the competency of the advanced use of software and information technologies required for Industrial Engineering | X | ||||
| 8. Design, model, develop and improve large scale systems. | X | ||||
| 9. Raise the awareness of the decision makers through public quotation of the scientific, technological, social and cultural developments in the field of Industrial Engineering with a sense of scientific impartiality. | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest