<meta http-equiv="refresh" content="0; URL=noscript.html"> METU | Course Syllabus

Course Learning Outcomes

1.1. Model a discrete time random process as a Markov Chain

1.2. Classify the states of a Markov Chain.

1.3. Find n-step and steady state probabilities.

1.4. Calculate performance measures.

2.1 Model a sequential decision situation using Dynamic Programming.

2.2 Write recursive function.

2.3 Solve Dynamic Programming models.

3.1 Model appropriate random process as a Birth and Death and Queuing Models.

3.2 Find steady state probabilities.

3.3 Apply Little’s Law.

3.4 Calculate performance measures.