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But when you call me Bayesian I know I’m not the only one

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Delivered by Andrew Gelman, Department of Statistics and Department of Political Science, Columbia University, at the inaugural New York R Conference in New York City at Work-Bench on Friday, April 44th, and Saturday, April 25th.

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But when you call me Bayesian I know I’m not the only one

  1. 1. But when you call me Bayesian I know I’m not the only one Andrew Gelman Department of Statistics and Department of Political Science, Columbia University New York R conference, 25 Apr 2015
  2. 2. “This week, the New York Times and CBS News published a story using, in part, information from a non-probability, opt-in survey sparking concern among many in the polling community. In general, these methods have little grounding in theory and the results can vary widely based on the particular method used.” — Michael Link, President, American Association for Buggy-Whip Manufacture Public Opinion Research
  3. 3. Xbox estimates, adjusting for demographics:
  4. 4. Karl Rove, Wall Street Journal, 7 Oct: “Mr. Romney’s bounce is significant.” Nate Silver, New York Times, 6 Oct: “Mr. Romney has not only improved his own standing but also taken voters away from Mr. Obama’s column.”
  5. 5. Xbox estimates, adjusting for demographics and partisanship:
  6. 6. Jimmy Carter Republicans and George W. Bush Democrats:
  7. 7. Adjusting for known differences between sample and population: Include more predictors Multilevel regression Poststratification
  8. 8. Annenberg 2000: Logit Annenberg 2004: Logit Annenberg 2008: Logit WhiteFemaleWhiteMaleWhiteGenderGap 25% 50% 75% 0% 7.5% 15%
  9. 9. Birthdays: The beginning
  10. 10. The published graphs show data from 30 days in the year
  11. 11. Chris Mulligan’s data graph: all 366 days
  12. 12. Matt Stiles’s heatmap
  13. 13. Aki Vehtari’s decomposition
  14. 14. The blessing of dimensionality We learned by looking at 366 questions at once! Consider the alternative . . .
  15. 15. Summary Big data . . . messy data Clean up messy data . . . Big model Big model . . . Bayesian inference Bayesian inference . . . Stan Understanding big models . . . R!

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