GLM glossary term #5047
GLM glossary term #5047
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side note, we should also choose glm or generalized linear models to use in tags: https://pymc-examples.readthedocs.io/en/latest/blog/tag/glm.html, https://pymc-examples.readthedocs.io/en/latest/blog/tag/generalized-linear-model.html
and I guess we could add links from glossary definitions to the correspondent tag in pymc-examples?
docs/source/glossary.md
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| Once you are satisfied with the model, posterior distribution can be summarized and interpreted. Common questions for the posterior include: intervals of defined boundaries, intervals of defined probability mass, and point estimates. When the posterior is very similar to the prior, the available data does not contain much information about a parameter of interest. | |||
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| - For more on generating and interpreting the posterior samples, see Statistical Rethinking 2nd Edition By Richard McElreath, chapter 3. | |||
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| Generalized Linear Model(GLM) | |||
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I would write generalized linear models and glm in different lines instead. Whatever we use as name in the glossary is what we have to use to reference it. Either or
{term}`glm`
{term}`generalized linear models`
is fine, but I'd rather not have to write both name and acronym every time.
this PR adds GLM to glossary.md