Summary
In this chapter, we began with an overview of probability. We covered the differences between conditional and independent probability and how Bayes’ Theorem leverages these concepts to provide a unique approach to probability modeling. Next, we discussed LDA, its assumptions, and how the algorithm can be used to apply Bayesian statistics to both perform classification modeling and supervised dimension reduction. Finally, we covered QDA, an alternative to LDA when linear decision boundaries are not effective.
In the next chapter, we will introduce the fundamentals of time-series analysis, including an overview of the depths and limitations of this approach to answering statistical questions.
