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SUMMARY:Matthias Sachs (Duke University)
DTSTART:20200811T120000Z
DTEND:20200811T130000Z
DTSTAMP:20260423T034448Z
UID:DSCSS/6
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/DSCSS/6/">No
 n-reversible Markov chain Monte Carlo for sampling of districting maps</a>
 \nby Matthias Sachs (Duke University) as part of Data Science and Computat
 ional Statistics Seminar\n\nAbstract: TBA\n\nFollowing the 2010 census exc
 essive 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 insta
 nces 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.\n
LOCATION:https://researchseminars.org/talk/DSCSS/6/
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