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DTSTART:20070311T020000
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DTSTAMP:20260924T212105
DTSTART;TZID=America/Detroit:20260925T093000
DTEND;TZID=America/Detroit:20260925T103000
SUMMARY:Workshop / Seminar:Political Communication Working Group (PCWG)
DESCRIPTION:The Political Communication Working Group (PCWG) is an interdisciplinary workshop for graduate students and faculty to convene and discuss how political messages are produced\, disseminated\, and interpreted in contemporary media environments. Our core research topics focus on the evolving relationships between media\, politics\, and society. Drawing on the interdisciplinary fields of communication\, political science\, sociology\, and information science\, our members explore a wide range of topics such as public support for entertainment censorship\, climate change communication\, social learning on TikTok\, political para-social relationships\, polarization\, political biases in AI-generated content\, etc. Our sessions encourage the exchange of ideas and work across theoretical and methodological traditions.
UID:152771-21914487@events.umich.edu
URL:https://events.umich.edu/event/152771
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Sessions
LOCATION:ISR 1460; Zoom: https://umich.zoom.us/j/96276603482[Meeting ID: 962 7660 3482; Passcode: 110221]
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20260910T125258
DTSTART;TZID=America/Detroit:20260925T100000
DTEND;TZID=America/Detroit:20260925T110000
SUMMARY:Workshop / Seminar:Beyond Rankings: Bayesian Learning of Parity\, Tiers\, and Dynamic Regimes
DESCRIPTION:Rankings are widely used to summarize comparative information\, but they can suggest differences that the data do not strongly support. In paired-comparison data\, two competitors may receive different ranks even when their underlying abilities are very similar. This raises a fundamental statistical question: when should competitors be ranked separately\, and when should they be regarded as statistically tied?\n\nIn this talk\, I will present a Bayesian framework for learning rank structure and competitive balance directly from paired-comparison data. A geometric constraint on latent abilities controls the extent of ability differences and allows rank tiers to emerge naturally when competitors are not meaningfully distinguishable. I will then extend the framework to longitudinal data using a hidden Markov model\, allowing competitive balance and rank structure to change across persistent historical regimes.\n\nI will discuss the statistical formulation\, computation\, and theoretical properties of the model\, and illustrate the approach with applications to the National Basketball Association and the English Premier League. These applications show how the framework can identify changes in competitive balance\, recover meaningful rank tiers\, and quantify uncertainty in rankings. More broadly\, the framework provides a way to ask not only who ranks higher\, but how much separation the data actually support.\n\n*Department reception in 450 West Hall after the seminar at 11:00 AM*
UID:151908-21912393@events.umich.edu
URL:https://events.umich.edu/event/151908
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Research,seminar,statistics,Talk
LOCATION:West Hall - 340
CONTACT:
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