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DTSTART:20211107T020000
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UID:calendar.2074.events_uoft_date.0@www.statistics.utoronto.ca
CREATED:20220201T204904Z
DESCRIPTION:\nWhen and Where: \nMonday, March 07, 2022 3:30 pm to 4:30 pm
  \n Online \n\nSpeakers \nAlicia Johnson \n\nDescription: \nIn a survey of
  high-ranking institutions, only 6 of 50 colleges and 45 of 102 universit
 ies offer an undergraduate course in Bayesian statistics (Dogucu and Hu, 
 2021). Given the rising popularity of Bayesian statistics, this absence f
 rom the common curriculum likely reflects an assumption that the topic can
 ’t be taught at the undergraduate level, not that the topic isn’t importa
 nt to undergraduate learners and comparably trained practitioners. In this
  talk, I’ll discuss our strategies to push back on this assumption in Bay
 es Rules! An Introduction to Applied Bayesian Modeling (Johnson, Ott, & 
 Dogucu, 2022). Beyond a careful consideration of mathematical level, the
 se strategies reflect our belief in the importance of inclusive, ethical\
 , and active learning pedagogy and the importance of being human.Please jo
 in the event.About Alicia JohnsonAlicia Johnson is an associate professor 
 of statistics at Macalester College, a liberal arts institution in St Pau
 l, Minnesota. Her research interests span Bayesian statistics, computati
 onal statistics, and statistics / data science education. Along with Mile
 s Ott and Mine Dogucu, she co-authored the book “Bayes Rules! An Introduc
 tion to Applied Bayesian Modeling” (2022), and is a strong believer that 
 the (Bayesian) statistics community can be broadened through more accessib
 le and inclusive pedagogy. Outside of statistics, Dr. Johnson enjoys buil
 ding things, four seasons, and any activity that involves fresh air (amo
 ng other things). \n\nContact Information: \n CANSSI Ontario \n\nCategorie
 s \n Data Science ARES \n\nAudiences \n FacultyGraduate Students
DTSTART;TZID=America/New_York:20220307T153000
DTEND;TZID=America/New_York:20220307T163000
LAST-MODIFIED:20220301T135014Z
SUMMARY:Demystifying Bayesian Statistics
URL;TYPE=URI:https://www.statistics.utoronto.ca/events/demystifying-bayesia
 n-statistics
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