Automated Redistricting Simulation Using Markov Chain Monte Carlo
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Publication:5066742
DOI10.1080/10618600.2020.1739532OpenAlexW3012513771MaRDI QIDQ5066742
Michael J. Higgins, Kosuke Imai, Benjamin Fifield, Alexander Tarr
Publication date: 30 March 2022
Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/10618600.2020.1739532
Metropolis-Hastings algorithmparallel temperinggerrymanderingsimulated temperinggraph cutsSwendsen-Wang algorithm
Related Items (11)
Sampling Algorithms for Discrete Markov Random Fields and Related Graphical Models ⋮ Constraint-based electoral districting using a new compactness measure: an application to Portugal ⋮ Redistricting optimization with recombination: a local search case study ⋮ Reconfiguration of connected graph partitions ⋮ Irreducibility of recombination Markov chains in the triangular lattice ⋮ Sequential Monte Carlo for Sampling Balanced and Compact Redistricting Plans ⋮ Metropolized Forest Recombination for Monte Carlo Sampling of Graph Partitions ⋮ A parallel evolutionary multiple-try Metropolis Markov chain Monte Carlo algorithm for sampling spatial partitions ⋮ Reconfiguration of connected graph partitions via recombination ⋮ Political districting to minimize cut edges ⋮ Metropolized Multiscale Forest Recombination for Redistricting
Uses Software
Cites Work
- Towards optimal scaling of Metropolis-coupled Markov chain Monte Carlo
- A tabu search heuristic and adaptive memory procedure for political districting
- An Optimization Based Heuristic for Political Districting
- Assessing significance in a Markov chain without mixing
- Annealing Markov Chain Monte Carlo with Applications to Ancestral Inference
- Equation of State Calculations by Fast Computing Machines
- Monte Carlo sampling methods using Markov chains and their applications
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