Judicious Judgment Meets Unsettling Updating: Dilation, Sure Loss, and Simpson's Paradox

@article{Gong2017JudiciousJM,
  title={Judicious Judgment Meets Unsettling Updating: Dilation, Sure Loss, and Simpson's Paradox},
  author={Ruobin Gong and Xiao-Li Meng},
  journal={arXiv: Statistics Theory},
  year={2017}
}
Statistical learning using imprecise probabilities is gaining more attention because it presents an alternative strategy for reducing irreplicable findings by freeing the user from the task of making up unwarranted high-resolution assumptions. However, model updating as a mathematical operation is inherently exact, hence updating imprecise models requires the user's judgment in choosing among competing updating rules. These rules often lead to incompatible inferences, and can exhibit unsettling… Expand

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