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  • $\begingroup$ From a practical point of view, I'd probably come up with a reasonable guess of the range of values to expect for alpha and beta, and then choose the hyper priors such that this range of values is well covered... $\endgroup$ Commented Jul 2, 2020 at 10:44
  • $\begingroup$ Also, you might want to look into Jeffrey's priors, which are - as I understand it - the canonical solution to choosing non-informative priors. en.wikipedia.org/wiki/Jeffreys_prior $\endgroup$ Commented Jul 2, 2020 at 10:44
  • $\begingroup$ @jhin Thanks. Jeffreys prior is a good one, but its support is not purely positive. $\endgroup$ Commented Jul 2, 2020 at 10:51
  • $\begingroup$ @jhin Yeah. Actually what the textbook does is just a reasonable guess. $\endgroup$ Commented Jul 2, 2020 at 10:52