University of Birmingham > Talks@bham > Data Science and Computational Statistics Seminar > Non-reversible Markov chain Monte Carlo for sampling of districting maps

Non-reversible Markov chain Monte Carlo for sampling of districting maps

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Following the 2010 census excessive Gerrymandering (i.e., the design of electoral districting maps in such a way that outcomes are tilted in favor of a certain political power/party) has become an increasingly prevalent practice in several US states. Recent approaches to quantify the degree of such partisan districting use a random ensemble of districting plans which are drawn from a prescribed probability distribution that adheres to certain non-partisan criteria. In this talk I will discuss the construction of non-reversible Markov chain Monte-Carlo (MCMC) methods for sampling of such districting plans as instances of what we term the Mixed skewed Metropolis-Hastings algorithm (MSMH)—a novel construction of non-reversible Markov chains which relies on a generalization of what is commonly known as skew detailed balance.

This talk is part of the Data Science and Computational Statistics Seminar series.

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