This course facilitates students in understanding of the fundamentals of Markov processes, one of most basic stochastic processes, through analyses of stochastic models.
At the end of this course, students will be able to:
1) Have understandings of the concept of Markov property in discrete and continuous time, and the basic facts that hold in Markov processes.
2) Apply the theory of Markov processes to analyze various stochastic models.
Markov processes, stochastic models, Markov chains, Poisson processes
|✔ Specialist skills
|Critical thinking skills
|✔ Practical and/or problem-solving skills
Slides and blackboard are used. Towards the end of class, students are given exercise problems related to what is taught on that day to solve.
|Markov property and discrete time Markov chains
|Explain the concept of Markov properties.
|Transition diagram and probability distributions of the state
|Explain the transition diagram and probability distribution of the state.
|Classification of the state: connectivity
|Classificate the state of Markov chains.
|Explain the concept and basic facts of the periodicity.
|Explain the concept and basic facts of the recurrence.
|Explain the concept of the stationary distributions and its derivation.
|Explain the limit theorems.
|Review the contents of classes 1-7.
|Understand the definition of Poisson processes and explain its basic properties.
|Compound Poisson processes
|Understand the definition of compound Poisson processes and explain its basic properties.
|Continuous time Markov chains
|Understand the definition of Markov chains in continuous time and explain its basic properties.
|Explain the basic properties and applications of birth-death processes.
|Explain the basic properties and applications of queueing systems.
|Control of Markov chains
|Explain the basic approach to control problems of Markov chains and its applications.
|Understand the definition of Brownian motion and explain its basic properties.
P. Brémaud, Markov Chains: Gibbs Fields, Monte Carlo Simulation, and Queues, Springer
Students will be assessed on the understanding of Markov processes and its application.
Students' course scores are based on the midterm exercise and the final exams.
It is preferable that students have completed MCS.T212:Fundamentals of Probability.