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

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• Matthias Sachs (Duke University)
• Tuesday 11 August 2020, 13:00-14:00
• https://zoom.us/j/3264147316?pwd=S1ZZMS81UzRjRnp3NlFTcWFPOVpnZz09.

If you have a question about this talk, please contact Hong Duong.

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.

This talk is included in these lists:

• Data Science and Computational Statistics Seminar
• School of Mathematics events
• https://zoom.us/j/3264147316?pwd=S1ZZMS81UzRjRnp3NlFTcWFPOVpnZz09

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