| 1. | Course Title | Monte-Carlo Simulations | |||||||||||
| 2. | Code | 4ФЕИТ08021 | |||||||||||
| 3. | Study program | 13-PMA, 16-MNT | |||||||||||
| 4. | Organizer of the study program (unit, institute, department) | Faculty of Electrical Engineering and Information Technologies | |||||||||||
| 5. | Degree (first, second, third cycle) | Second cycle | |||||||||||
| 6. | Academic year/semester | I/1 | 7. | Number of ECTS credits | 6.00 | ||||||||
| 8. | Lecturer | Dr Sonja Gegovska – Zajkova | |||||||||||
| 9. | Course Prerequisites | ||||||||||||
| 10. | Course Goals (acquired competencies):
The student will be able to effectively apply the Monte-Carlo method for creative problem solving related to data analysis and interpretation of statistical models in a different context. The student will be able to clearly express the assumptions on which certain conclusions are based, using the Monte Carlo method which systematically and critically examines the assumptions using an analytical approach. He/she will be able to optimize using stochastic simulation. |
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| 11. | Course Syllabus:
Monte Carlo experiments. Estimating using the arithmetic mean of a sample, estimating the sample variance, generating random numbers with P, generating quasi-random numbers with uniform and non-uniform distribution. Generation of discrete and continuous random variables. Statistical analysis of simulated data. Techniques for reducing dispersion. Bootstrapping method. Simulation of random processes. Generating trajectories of Markov processes; Monte Carlo Markov chains. Computer-intensive techniques for estimating standard error, confidence intervals, hypothesis testing, and prediction error. Stochastic optimization; Stochastic approximation methods with simulation, evaluation of gradients, etc. |
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| 12. | Learning methods:
Evaluation, synthesis, analysis and application |
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| 13. | Total number of course hours | 180 | |||||||||||
| 14. | Distribution of course hours | 3 + 3 | |||||||||||
| 15. | Forms of teaching | 15.1 | Lectures-theoretical teaching | 45 hours | |||||||||
| 15.2 | Exercises (laboratory, practice classes), seminars, teamwork | 45 hours | |||||||||||
| 16. | Other course activities | 16.1 | Projects, seminar papers | 30 hours | |||||||||
| 16.2 | Individual tasks | 30 hours | |||||||||||
| 16.3 | Homework and self-learning | 30 hours | |||||||||||
| 17. | Grading | ||||||||||||
| 17.1 | Exams | points | |||||||||||
| 17.2 | Seminar work/project (presentation: written and oral) | 50 points | |||||||||||
| 17.3. | Activity and participation | 20 points | |||||||||||
| 17.4. | Final exam | 30 points | |||||||||||
| 18. | Grading criteria (points) | up to 50 points | 5 (five) (F) | ||||||||||
| from 51 to 60 points | 6 (six) (E) | ||||||||||||
| from 61 to 70 points | 7 (seven) (D) | ||||||||||||
| from 71 to 80 points | 8 (eight) (C) | ||||||||||||
| from 81 to 90 points | 9 (nine) (B) | ||||||||||||
| from 91 to 100 points | 10 (ten) (A) | ||||||||||||
| 19. | Conditions for acquiring teacher’s signature and for taking final exam | None | |||||||||||
| 20. | Forms of assessment | Successfully prepared and defended seminar paper | |||||||||||
| 21. | Language | Macedonian and English | |||||||||||
| 22. | Method of monitoring of teaching quality | Self-evaluation | |||||||||||
| 23. | Literature | ||||||||||||
| 23.1. | Required Literature | ||||||||||||
| No. | Author | Title | Publisher | Year | |||||||||
| 1. | Sheldon M.Ross | Simulation (4th ed.) | Elsevier Academic Press | 2006 | |||||||||
| 23.2. | Additional Literature | ||||||||||||
| No. | Author | Title | Publisher | Year | |||||||||
| 1. | James E. Gentle | Random Number Generation and Monte Carlo Methods | Springer | 2003 | |||||||||
