42 citations · 84 across the 15 of their papers we have counts for
16 papers
The Balanced Up-Down Walk
Hugo A. Akitaya, Sarah Cannon, Gregory Herschlag +3
Markov chains based on spanning trees have been hugely influential in algorithms for assessing fairness in political redistricting. The input graph represents the geographic buildi…
A Cycle Walk for Sampling Measures on Spanning Forests for Redistricting
Daryl R. DeFord, Gregory Herschlag, Jonathan C. Mattingly
We introduce the Cycle Walk, a new Markov chain Monte Carlo method for sampling distributions on balanced graph partitions, motivated by applications in political redistricting. Th…
Multiscale Parallel Tempering for Fast Sampling on Redistricting Plans
Gabriel Chuang, Gregory Herschlag, Jonathan C. Mattingly
When auditing a redistricting plan, a persuasive method is to compare the plan with an ensemble of neutrally drawn redistricting plans. Ensembles are generated via algorithms that…
Mathematically Quantifying Non-responsiveness of the 2021 Georgia Congressional Districting Plan
Zhanzhan Zhao, Cyrus Hettle, Swati Gupta +3
To audit political district maps for partisan gerrymandering, one may determine a baseline for the expected distribution of partisan outcomes by sampling an ensemble of maps. One a…
Multi-Scale Merge-Split Markov Chain Monte Carlo for Redistricting
Eric A. Autry, Daniel Carter, Gregory Herschlag +2
We develop a Multi-Scale Merge-Split Markov chain on redistricting plans. The chain is designed to be usable as the proposal in a Markov Chain Monte Carlo (MCMC) algorithm. Samplin…
Non-reversible Markov chain Monte Carlo for sampling of districting maps
Gregory Herschlag, Jonathan C. Mattingly, Matthias Sachs +1
Evaluating the degree of partisan districting (Gerrymandering) in a statistical framework typically requires an ensemble of districting plans which are drawn from a prescribed prob…