Adding a generalised normal distribution #278
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…luded. This follows broadly the Wikipedia page for notation - this differs slightly from the implementation in scipy.stats, but the quantitative results are identical as far as I can see. This only implements the symmetric distribution - in accorandance with the naming of normal distributions in pymc I have opted against including Symmetric in the name. It would also be moderately easy to implement the asymmetric distribution based on what is included here.
…GeneralizedNormal
This is normal! PyMC uses PyTensor which return symbolic expressions until actually evaluated. You can call |
ricardoV94
reviewed
Dec 11, 2023
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| # moment here returns the mean | ||
| def moment(rv, size, mu, alpha, beta): | ||
| moment, _ = pt.broadcast_arrays(mu, alpha, beta) |
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| moment, _ = pt.broadcast_arrays(mu, alpha, beta) | |
| moment, *_ = pt.broadcast_arrays(mu, alpha, beta) |
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Also missing the moment test
I had accidentally added an extra division by two in logp and a similar in logcdf. The results from the codes have been double-checked with scipy.stats.gennorm
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Thanks for the feedback - in trying to get things working I had introduce two minor bugs in logp and logcdf. These are now fixed and double-checked to agree with scipy.stats.gennorm and a moment test has been added. |
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This pull request implements a generalized Gaussian distribution (https://en.wikipedia.org/wiki/Generalized_normal_distribution) - this is useful for situations where the distribution is close to Gaussian but with some kurtosis.
The distribution implemented is the symmetric version, but in keeping with the naming of the normal distribution I have not included this in the naming.
I have tried to follow the instructions for adding new distributions but for some reason when I do e.g.
which is an odd outcome since all intermediate steps are correct and the code works when embedded outside of
pymc-experimental. As a consequence the tests do not appear to work.