This quarter I am TAing UCLA’s Statistics 102C. Introduction to Monte Carlo Methods for Professor Qing Zhou. This course did not exist when I was an undergraduate, and I think it is pretty rare to teach Monte Carlo (minus the bootstrap if you count that) or MCMC to undergrads. I am excited about this class because to me, MCMC turns Statistics on its head. It felt like a totally different paradigm compared to the regression and data analysis paradigm that I was used to at the time. It also exposes students to the connection between Statistics/MCMC and other fields such as Computer Science, Genetics/Biology, etc.
I usually do not have much to talk about during week 1, especially if my class is the second day of the quarter. Today was an exception because I wanted to excite the class about this topic.
Some examples I discussed:
- the general recipe for Monte Carlo methods
- the bootstrap as an example of resampling, and R loops
- computing
and mention of Buffon’s Needle
- scheduling/timetabling and occupancy/matching problems using stochastic search (simulated annealing, Tabu search etc.)
- mention of genetic algorithms and swarm intelligence
- PageRank as a Markov process
- drawing a random sample of web pages using Random Walk Metropolis-Hastings
- short inventory of fields and situations where MCMC is popular
You can see my handout here.
Keep the handouts coming! This will help supplement the simulation/MCMC course I’m taking in Biostat. Especially the R computation part!
Feel free to use my exploratory statistics notes I wrote for my R undergrad class.