Why no log-normal distributions in the examples? about bap3 HOT 3 CLOSED

I am not familiar with the term "Jeffreys variables". The log-normal is indeed a very common distribution. In the book we only discuss a small subset of distributions, this subset includes many commonly used distributions but others are excluded. PyMC and PreliZ include many more distributions. There is some discussion of prior/likelihood elicitation in the book but it's not very deep.

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Thanks for the explanation.
I realized that the gamma distribution is somewhat similar but more general than the log-normal distribution.
So it might be the reason why you did not use the log-normal.
BTW, the gamma distribution is introduced in code 2.27 but again, not very much described.

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Yeah, I tried to list the most commonly used distribution in the book, but that list could be biased. In practice, I tend to use the halfnormal as a vague default prior and I tend to switch to Gamma (with mu, sigma parametrization) if I have more information to define a prior.

Something I would like to see is a survey of the most commonly used distributions in probabilistic models. Not sure how easy would be to compile such a list. Also it would be interesting to have some kind of granularity, by discipline, by probabilistic programming language, etc.

I have been thinking of integrating something like this https://distribution-explorer.github.io/discrete/bernoulli.html or even some of the information in Wikipedia (that can be very complete for many distributions) into PreliZ. PreliZ's documentation already includes some information about the distributions https://preliz.readthedocs.io/en/latest/api_reference.html. But We could do better.

I have a colleague working on a site that will collect commonly used priors for different types of models. The site is not yet public, but hopefully, it will be soon.

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