Statistical Applications

We talked briefly about some statistics in previous weeks, but there are even more statistical ideas to consider.

Bayesian Models

A Bayesian Model is a type of machine learning based on Bayes Theorem. In fact, almost all of machine learning is based on statistics, which itself is based in probability. Below is a short video on the Naive Bayes Classifier.

Stochastic Models

The entire field of Stochastic models is based around probability. Stochastic models allow us to model a number of different real-life processes, usually ones that tend to be "unpredictable" like investments, or the stock market. Unfortunately, much of stochastic modeling involves statsitics, but the below explains how Markov Chains work. Note that this requires some graph theory as well!

There is a small amount of matrix arithmetic. It's not important that you understand exactly how it works, but if you know about eigenvectors, you can gain a deeper understanding.