## Opening Statements on Markov Chain Monte Carlo

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 […]

## Be Careful Searching Python Dictionaries!

For my talk on High Performance Computing in R (which I had to reschedule due to a nasty stomach bug), I used Wikipedia linking data, an adjacency list of articles and the articles to which they link. This data was linked from DataWrangling and was originally created by Henry Haselgrove. The dataset is small on disk, but I needed a dataset that was huge, very huge. So, without a simple option off the top of my head, I took this data and expanded a subset of it into an incidence matrix, occupying 2GB in RAM. Perfect!

The subsetting was a bit of a problem because I had to maintain the links within the subgraph induced by the same. This required me to search dictionary objects for keys. This is where things went awry. Usually, I try to be efficient as possible. Since I was just producing a little example, and would never ever run this code otherwise, I wasn’t as careful.

The data were presented as a follows

1. First, I looked at from, and if from was in the chosen subset, keep it and proceed to 2, otherwise, throw it out.
2. Then, take the to nodes and only keep the nodes that […]

## Exact Complexity of Mergesort, and an R Regression Oddity

It’s nice to be back after a pretty crazy two weeks or so.

Let me start off by stating that this blog post is simply me pondering and may not be correct. Feel free to comment on inaccuracies or improvements!

In preparation for an exam and my natural tendencies to be masochistic, I am forcing myself to find the exact complexities of some sorting algorithms and I decided to start with a favorite – mergesort. Mergesort divides an array or linked list first into two halves (or close to it) and then recursively divides the successive lists into halves until it ends up with two lists containing 1 element each – the base case. The elements are then compared and switched so that they are in order, and form their own list.

At successive levels we compare the last element of the first sublist to the first element of the second sublist and merge them together to form another list. This process continues up the recursion tree until the entire original list is sorted. For a more comprehensive and precise description, see this article on mergesort.

Easy: The Worst Case

The worst case is easy as any CS student will tell you. Looking […]