Skip to content
How many voters for each candidate in first round of election, voted for candidate X in second round? Ukrainian presidential election-2019 example
Branch: master
Clone or download
Latest commit 3c3a3e1 May 21, 2019
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
data init May 4, 2019
img picture for poll May 21, 2019
README.md Update README.md May 21, 2019
main.R init May 4, 2019
model.R init May 4, 2019

README.md

bayes_vote_transfer

Use bayesian linear regression to find how many voters for each candidate in first round of election, voted for candidate X in second round? Based on real data from each poll station on Ukrainian presidential election-2019.

What are the assumptions of a model:

  • all standart assumptions for bayesian linear regression
  • there are different levels of support for each candidates in second round from different group of voters in first round (each group - voters who voted for some candidate) (uniforms as priors)
  • turnout modelled for each such group as different parameters, too (student as priors)
  • final share of voters for candidate Z from first tour, who voted for candidate X in second, is a multiple of level of support for candidate X and turnout for this group of voters: total_x * turnout
  • finally, on each poll station, sum of all total_x[i] * turnout[i] * votes_from_first_round[i] == number of votes for X in second round, where i - index of each different group of voters, and votes_from_first_round[i] - rezult for group i on this station.

Result for Zelensky

Result for Poroshenko

Also, see here in Ukrainian:

You can’t perform that action at this time.