STAT376 STOCHASTIC PROCESSES
| Course Code: | 2460376 |
| METU Credit (Theoretical-Laboratory hours/week): | 5 (4.00 - 2.00) |
| ECTS Credit: | 6.0 |
| Department: | Statistics |
| Language of Instruction: | English |
| Level of Study: | Undergraduate |
| Course Coordinator: | Prof.Dr. AYŞEN AKKAYA |
| Offered Semester: | Spring Semesters. |
Course Objectives
1. Provide fundamental knowledge of stochastic models. Develop modeling skills for decisions made under uncertainty.
2. Learn theoretical foundations and application areas of most frequently used stochastic processes and optimization methods. Hence, the acquisition of the following sub-skills is targeted:
a) Model a given process as a Markov chain, calculate system performance measures based on the model, and choose between alternative system configurations and policies.
b) For a given queueing system; calculate performance measures, make improvements according to these performance measures, and choose between alternative queueing system configurations.
3. Ability to make decisions under uncertainty using utility theory and decision trees.
Course Content
Rewiev of Probability. Theory Markov Chains. Discrete and Continuous time Markov Chains. Poisson Process. Queuning Processes. Birth and Death Processes. Decision Analysis.
Course Learning Outcomes
(a) an ability to apply knowledge of mathematics, science, and statistics
(b) an ability to design and conduct experiments, as well as to analyze and interpret data
(c) an ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability
(e) an ability to identify, formulate, and solve statistical problems
(k) an ability to use the techniques, skills, and modern statistics tools necessary for statistics practice
Program Outcomes Matrix
| Level of Contribution | |||||
| # | Program Outcomes | 0 | 1 | 2 | 3 |
| 1 | Applying the knowledge of statistics, mathematics and computer to statistical problems and developing analytical solutions. | ✔ | |||
| 2 | Defining, modeling and solving real life problems that involve uncertainty, and interpreting results. | ✔ | |||
| 3 | To decide on the data collection technique, and apply it through experiment, observation, questionnaire or simulation. | ✔ | |||
| 4 | Analysing small and big volumes of data and interpreting results. | ✔ | |||
| 5 | Utilizing up-to-date techniques, computer hardware and software required for statistical applications; developing software programs and numerical solutions for specific problems when necessary. | ✔ | |||
| 6 | Taking part in intradisciplinary and interdisciplinary teamwork, using time efficiently, taking leadership responsibilities and being entrepreneurial. | ✔ | |||
| 7 | Taking responsibility in individual work and offering authentic solutions. | ✔ | |||
| 8 | Following contemporary developments and publications in statistical science, conducting research, being open to novelty and thinking critically. | ✔ | |||
| 9 | Efficiently communicating in Turkish and English to define and analyze statistical problems and to interpret the results. | ✔ | |||
| 10 | Having a professional and ethical sense of responsibility. | ✔ | |||
| 11 | Developing computational solutions to statistical problems that cannot be solved analytically. | ✔ | |||
| 12 | Having theoretical background and developing new theories in statistics, building relations between theoretical and practical knowledge. | ✔ | |||
| 13 | Serving the society with the expertise in the field. | ✔ | |||
0: No Contribution 1: Little Contribution 2: Partial Contribution 3: Full Contribution