Closed PaulScemama closed 1 year ago
Merging #1480 (0b66d2a) into main (4a687c0) will increase coverage by
0.00%
. The diff coverage is100.00%
.:exclamation: Current head 0b66d2a differs from pull request most recent head eda2f50. Consider uploading reports for the commit eda2f50 to get more accurate results
"the InvGamma(shape, scale) is equivalent to taking the reciprocal of samples from a Gamma(shape, 1 / scale) distribution" in the docs.
Yes, definitely.
This PR is a draft to close #1368. Furthermore, I had to change the
.pre-commit-config.yaml
slightly to pass themypy
check. This bug has been discussed in #1474 (reply in thread).
Is that still an issue now that https://github.com/aesara-devs/aesara/pull/1482 is merged?
This PR is a draft to close #1368. Furthermore, I had to change the
.pre-commit-config.yaml
slightly to pass themypy
check. This bug has been discussed in #1474 (reply in thread).Is that still an issue now that #1482 is merged?
It is not an issue anymore.
Thanks a lot, @PaulScemama!
Thank you! @brandonwillard I learned a lot.
This PR is a draft to close https://github.com/aesara-devs/aesara/issues/1368. Furthermore, I had to change the
.pre-commit-config.yaml
slightly to pass themypy
check. This bug has been discussed in https://github.com/aesara-devs/aesara/discussions/1474#discussioncomment-5362160.@brandonwillard @rlouf let me know what you both think regarding the
types-setuptools
issue. If it hasn't affected anyone else then I would assume you wouldn't want the change to the.pre-commit-config.yaml
.Here are a few important guidelines and requirements to check before your PR can be merged:
pre-commit
is installed and set up.EDIT:
Something that I didn't realize in the beginning...from wolfram:
"the inverse gamma distribution with shape parameter $\alpha$ and scale parameter $\beta$ is the distribution followed by the inverse of a gamma distribution with shape parameter $\alpha$ and scale parameter $\color{red} 1 / \beta$."
Could be useful to say something like:
"the InvGamma(shape, scale) is equivalent to taking the reciprocal of samples from a Gamma(shape, 1 / scale) distribution" in the docs.