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.