Using Objective Bayesian Inference to Interpret Election Polls | by Ryan Burn | Oct, 2024


How to build a polls-only objective Bayesian model that goes from a state polling lead to probability of winning the state

With the presidential election approaching, a question I, and I expect many others have, is does a candidate’s polling in a state translates to their probability of winning the state.

In this blog post, I want to explore the question using objective Bayesian inference ([3]) and election results from 2016 and 2020. The goal will be to build a simple polls-only model that takes a candidate’s state polling lead and produces a posterior distribution for the probability of the candidate winning the state

Figure 1: An example posterior distribution for predicted win probability using FiveThirtyEight polling data from 2016 and 2020 ([1, 2]) and a snapshot of polling in Pennsylvania. The figure also shows the 5-th, 50-th, and 95-th percentiles of the prediction posterior distribution. Figure by author.

where the posterior distribution measures our belief in how predictive polls are.

For the model, I’ll use logistic regression with a single unknown weight variable, w:

Taking the 2020 and 2016 elections as observations and using a suitable prior, π, we can then produce a posterior distribution for the unknown weight

where

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#Objective #Bayesian #Inference #Interpret #Election #Polls #Ryan #Burn #Oct


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