Module Description
This module introduces the concept of modelling dependence and focuses on discrete-time Markov chains. Major topics: discrete-time Markov chains, examples of discrete-time Markov chains, classification of states, irreducibility, periodicity, first passage times, recurrence and transience, convergence theorems and stationary distributions
Lecture Topics
- Probability
- Random Variables
- Expectation
- Variance
- Conditional Expectations
- Applications of conditional expectation
- Introduction to Markov Chains
- Chapman-Kolmogorov Equations
- Classification of States
- Long time behaviours
- Time Reversibility
- Branching Processes
This will be your first exposure to Markov Chains. It is a very interesting topic and fun to play with.
If you are a statistics major, make sure you pick this module in semester 1. Semester 1 will be taught by statistics department and will not be that rigorous on the proofs. Semester 2 will be taught by mathematics department. More theoretical, more proofs.
Workload
Two Two-hours lecture
Lectures were taught by Dr David Chew. He is generally a good lecturer. Very helpful and patient lecturer.
One two-hours tutorial
Tutorials were held in the classrooms. The tutorials are generally not difficult. Like I said, MC is very interesting and it's a joy to solve the questions.
Assessment
20% 2 online assignments
20% Term Test
60% Final Exam
The two online assignments are conducted on IVLE. Check your answers with friends.
Final paper will be a 2 hours closed book paper. You will be allowed to carry two pieces of cheatsheet.
Personal
I found the topics are very interesting and maybe that's why I was able to do better. I also found the questions in the final paper do-able. As long as you are careful in your calculation, it should be alright. Double check and make sure everything tallies. Have fun with the transition matrices. :)
Extra notes to readers: I have lecture notes, tutorials and past year papers. Download them from my dropbox.
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