sample_posterior {elections.dtree} R Documentation

## Draw election outcomes from the posterior distribution.

### Description

sample_posterior draws sets of ballots from independent realizations of the Dirichlet-tree posterior, then determines the probability for each candidate being elected by aggregating the results of the social choice function. See Everest et al. (2022) for details.

### Usage

sample_posterior(
dtree,
n_elections,
n_ballots,
n_winners = 1,
replace = FALSE,
)


### Arguments

 dtree A dirichlet_tree object. n_elections An integer representing the number of elections to generate. A higher number yields higher precision in the output probabilities. n_ballots An integer representing the total number of ballots cast in the election. n_winners The number of candidates elected in each election. replace A boolean indicating whether or not we should re-use the observed ballots in the monte-carlo integration step to determine the posterior probabilities. n_threads The maximum number of threads for the process. The default value of NULL will default to 2 threads. Inf will default to the maximum available, and any value greater than or equal to the maximum available will result in the maximum available.

### Value

A numeric vector containing the probabilities for each candidate being elected.

### References

Everest F, Blom M, Stark PB, Stuckey PJ, Teague V, Vukcevic D (2022). “Ballot-Polling Audits of Instant-Runoff Voting Elections with a Dirichlet-Tree Model.” doi:10.48550/ARXIV.2209.03881..

Everest F, Blom M, Stark PB, Stuckey PJ, Teague V, Vukcevic D (2022). “Auditing Ranked Voting Elections with Dirichlet-Tree Models: First Steps.” doi:10.48550/ARXIV.2206.14605..

[Package elections.dtree version 1.1.2 Index]