Bayesian Model for Predicting Voting Behavior in Presidential Elections in Kenya

This study applies the Bayesian Dirichlet-Multinomial model to predict Kenya’s 2022 presidential election. The model incorporates voter age, gender, poverty rate, and political ideation as major determinants of voting behavior. The model estimated Raila Odinga’s vote share at 48.17% (actual: 48.85%) and William Ruto’s at 46.96% (actual: 50.49%). It slightly overestimated minor candidates but proved more accurate than polls. A Bayesian p-value of 0.487 confirmed model reliability. Bayesian inference demonstrated superior adaptability by quantifying uncertainty and updating probabilities. Key voter behavior determinants included youthful population and political ideation. The study concludes that Bayesian modeling enhances election forecasting and recommends integrating it with machine learning, social media sentiment analysis, and economic indicators for improved predictions.