title: Bayesian inference on proportional elections.
abstract: Polls for majoritarian voting systems usually show estimates of the percentage of votes for each candidate. However, proportional vote systems do not necessarily guarantee the candidate with the most percentage of votes will be elected. Thus, traditional methods used in majoritarian elections cannot be applied on proportional elections. In this context, the purpose of this paper was to perform a Bayesian inference on proportional elections considering the Brazilian system of seats distribution. More specifically, a methodology to answer the probability that a given party will have representation on the chamber of deputies was developed. Inferences were made on a Bayesian scenario using the Monte Carlo simulation technique, and the developed methodology was applied on data from the Brazilian elections for Members of the Legislative Assembly and Federal Chamber of Deputies in 2010. A performance rate was also presented to evaluate the efficiency of the methodology. Calculations and simulations were carried out using the free R statistical software.
authors.affiliation: Department of Statistics, University of Brasília, Campus Darcy Ribeiro, Brasília-DF, Brazil.
authors.forename: Gabriel Hideki Vatanabe
authors.initials: GH
authors.lastname: Brunello
authors.affiliation: Department of Statistics, University of Brasília, Campus Darcy Ribeiro, Brasília-DF, Brazil.
authors.forename: Eduardo Yoshio
authors.initials: EY
authors.lastname: Nakano
mesh_terms: D001938:Brazil; D006801:Humans; D008962:Models, Theoretical; D011057:Politics