A Bayesian Model for the Prediction of United States Presidential Elections

Presented by:
Brittany Alexander

Using a combination of polling data and previous election results, Nate Silver successfully predicted the Electoral College distribution in the presidential election in 2008 with 98% accuracy and in 2012 with 100% accuracy. This study applies a Bayesian analysis of polls, assuming a normal posterior using a conjugate prior. The data were taken from the Huffington Post’s Pollster. States were divided into categories based on past results and current demographics. Each category used a different poll source for the prior. This model was originally used to predict the 2016 election, but later it was applied to the poll data for 2008 and 2012. For 2016, the model had 88% accuracy for the 50 states. For 2008 and 2012, the model had the same Electoral College Prediction as Nate Silver. The method of using state and national polls as a prior in election prediction seems promising and further study is needed.