Discrete-time Markov Chains and Poisson Processes

In this course we will cover discrete-time Markov chains and Poisson Processes. Knowledge of calculus and basic probability is essential for this course. The mathematical rigor of the course will be at an undergraduate level. We will cover from basic definition to limiting probabilities for discrete -time Markov chains. We will discuss in detail Poisson processes, the simplest example of a continuous-time Markov chain. The course will involve a lot of illustrative examples and worked out problems. PRE-REQUISITE : Basic Probability,Calculus INDUSTRY SUPPORT : Supply Chain, Communications. INTENDED AUDIENCE : Undergraduate students of Science and Engineering. Many postgraduate students as well as industry professionals dealing with stochastic modelling may find the course useful.

Week 1:Introduction to Discrete-time Markov ChainsWeek 2:Communication
Week 3:Hitting Times
Week 4:Classification of StatesWeek 5:Stationary DistributionWeek 6:Limit TheoremsWeek 7:Exponential Distribution and Counting ProcessesWeek 8:Poisson Processes