In this post, I want to talk about conditional dependence and independence between events. This is an important concept in probability theory and a central concept for graphical models. In my two-part post on Bayesian belief networks, I introduced … [Continue reading]
In the first part of this post, I gave the basic intuition behind Bayesian belief networks (or just Bayesian networks) — what they are, what they're used for, and how information is exchanged between their nodes. In this post, I'm going to show the … [Continue reading]
The moment of truth! The year-long battle for the White House is coming to its conclusion! Tomorrow, voters in all states will determine the next US president in one of the most unpredictable elections in US history. Many people will be … [Continue reading]
In my introductory Bayes' theorem post, I used a "rainy day" example to show how information about one event can change the probability of another. In particular, how seeing rainy weather patterns (like dark clouds) increases the … [Continue reading]
This is an update to the main post from September 20. Please click on the link for a detailed description of the election process and how we model it to make the predictions you see below. Click here to go back to the daily predictions page. You … [Continue reading]
This is an update to the main post from two weeks ago. Please click on the link for a detailed description of the election process and how we model it to make the predictions you see below. You can click on the date above the map to check the … [Continue reading]