[kaitotst]

Lecturer: Dr. Shui Feng

Despite having a requisite for Real Analysis (MATH 3A03), the amount of real analysis needed for this course is very minimal (or not at all haha). A challenging course that takes concepts taken from STATS 2D03 and views them in a very different perspective. The topics covered in this course were,

1. Measure Theory (indicator functions, algebras, sigma-algebras, set functions)
2. Probability Space (Law, Random Variables)
3. Convergence Types (distribution, probability, almost surely)
4. Weak and Strong Law of Large Number
5. Central Limit Theorem (Classical CLT and Lyapounov's CLT)
6. Not tested but mentioned: Poisson Convergence, Infinitely Divisible Law

This course is very computation heavy in the sense that you have to know the conditions of a theorem very well, use it and to show it computes to a certain value (not necessarily a number like in calculus). This course was very intimidating at first since everything felt so abstract, but once you got used to the new notation/language, everything will become clear and pretty trivial (i.e. understanding indicator function notation, bridging that connection with measure theory).

Unlike the 2nd/3rd year stats courses, this course makes you think A LOT and you'll need to have a strong understanding of his in-class examples/assignments to do well. You won't need to memorize theorems/definitions too since everything in this course was open book (from midterms to final exam, but you weren't allowed to use electronic devices). Assignments were very tricky; I suggest collaborating with friends, going on StackExchange and going to his office hours to really understand the way to solve these problems and understand them for the midterm/exam. Also, always attend class, because he gives very important examples that may be relevant to the assignments.

I feel like this course could've been much harder if it weren't for Dr. Feng since he dumbed everything down by a lot. His lectures may be a little dry at times and his accent might be an issue for people, but overall, learning higher level probability was a lot of fun and it was one of the easier 4th year stats courses (but do people even take 4th year stats?, there were like 30 people in the class and about 40% were graduate students).

Mark Distribution:

20% for 5 Assignments (4% each)
30% for 2 Midterms (15% each)
50% Exam