Forecasting the 2012 and 2014 Elections Using Bayesian Prediction and Optimization

Steven E. Rigdon, Jason J. Sauppe, Sheldon H. Jacobson

Research output: Contribution to journalArticlepeer-review


This article presents a data-driven Bayesian model used to predict the state-by-state winners in the Senate and presidential elections in 2012 and 2014. The Bayesian model takes into account the proportions of polled subjects who favor each candidate and the proportion who are undecided, and produces a posterior probability that each candidate will win each state. From this, a dynamic programming algorithm is used to compute the probability mass functions for the number of electoral votes that each presidential candidate receives and the number of Senate seats that each party receives. On the final day before the 2012 election, the model gave a probability of (essentially) one that President Obama would be reelected, and that the Democrats would retain control of the U.S. Senate. In 2014, the model gave a final probability of.99 that the Republicans would take control of the Senate.

Original languageEnglish (US)
JournalSAGE Open
Issue number2
StatePublished - Jun 19 2015


  • Bayesian model
  • dynamic programming
  • electoral college
  • posterior distribution
  • senate elections

ASJC Scopus subject areas

  • General Arts and Humanities
  • General Social Sciences


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