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X-WR-CALNAME;VALUE=TEXT: Sampling partitions, with applications to redistricting
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SUMMARY: Sampling partitions, with applications to redistricting
DESCRIPTION:<p>	Speaker: <strong>Moon Duchin</strong> from the MGGG Redistricting Lab at Tufts. She will be speaking <strong>in-person</strong> on:</p><p>	<em>Sa</em><em>mpling partitions, with applications to redistricting</em></p><p>	<span><span><span style="color:black">Abstract: In the world of gerrymandering, there are many reasons to want to take a “representative sample” of the space of balanced graph partitions — that is, a sample pulled from a known distribution which is clearly relevant to the political districting problem. There are numerous Markov chain methods, plus sequential Monte Carlo, plus a direct tree decomposition method, at least. I’ll discuss this modeling problem at a high level, and will also offer some tools and diagnostics for comparisons.</span></span></span></p>
LOCATION:SEC 1.413, and streamed via Zoom at: https://harvard.zoom.us/j/95184948637?pwd=bXBIc2U5MEZ0QmRUb01WQ0o0SXRCdz09
STATUS:CONFIRMED
DTSTART:20221021T170000Z
DTEND:20221021T183000Z
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