56 citations · 125 across the 10 of their papers we have counts for
10 papers
Convergence of Stratified MCMC Sampling of Non-Reversible Dynamics
Gabriel Earle, Jonathan Mattingly
We present a form of stratified MCMC algorithm built with non-reversible stochastic dynamics in mind. It can also be viewed as a generalization of the exact milestoning method, or…
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…
Metropolized Forest Recombination for Monte Carlo Sampling of Graph Partitions
Eric Autrey, Daniel Carter, Gregory Herschlag +2
We develop a new Markov chain on graph partitions that makes relatively global moves yet is computationally feasible to be used as the proposal in the Metropolis-Hastings method. O…
Numerical methods for stochastic differential equations based on Gaussian mixture
Lei Li, Jianfeng Lu, Jonathan Mattingly +1
We develop in this work a numerical method for stochastic differential equations (SDEs) with weak second order accuracy based on Gaussian mixture. Unlike the conventional higher or…
Limiting Behaviors of High Dimensional Stochastic Spin Ensembles
Yuan Gao, Kay Kirkpatrick, Jeremy Marzuola +2
Lattice spin models in statistical physics are used to understand magnetism. Their Hamiltonians are a discrete form of a version of a Dirichlet energy, signifying a relationship to…