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Closes #5514

Major / Breaking Changes

  • ...

Bugfixes / New features

  • Fix bug when passing a list or tuple of variables as the p parameter of Categorical and Multinomial distributions

Docs / Maintenance

  • ...

@ricardoV94 ricardoV94 requested a review from Armavica November 3, 2022 17:19
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codecov bot commented Nov 3, 2022

Codecov Report

Merging #6265 (1b5ad99) into main (48664c0) will increase coverage by 0.00%.
The diff coverage is 100.00%.

Additional details and impacted files

Impacted file tree graph

@@           Coverage Diff           @@
##             main    #6265   +/-   ##
=======================================
  Coverage   94.19%   94.19%           
=======================================
  Files         102      102           
  Lines       21477    21487   +10     
=======================================
+ Hits        20230    20240   +10     
  Misses       1247     1247           
Impacted Files Coverage Δ
pymc/distributions/discrete.py 99.22% <100.00%> (+<0.01%) ⬆️
pymc/distributions/multivariate.py 92.28% <100.00%> (+0.02%) ⬆️
pymc/tests/distributions/test_discrete.py 98.88% <100.00%> (+<0.01%) ⬆️
pymc/tests/distributions/test_mixture.py 99.35% <100.00%> (ø)
pymc/tests/distributions/test_multivariate.py 99.45% <100.00%> (+<0.01%) ⬆️

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Thank you for fixing this bug! This seems fine to me. It works for my issue and also for the slightly more complex model which was behind.

@ricardoV94 ricardoV94 force-pushed the categorical_multinomial_check_bugs branch from 77db156 to 1b5ad99 Compare November 4, 2022 08:56
- Fixes bug that occurred when constructing probability parameters from lists or tuples of Aesara variables
- Improves logp checks for valid probabilty parameters
@ricardoV94 ricardoV94 force-pushed the categorical_multinomial_check_bugs branch from 1b5ad99 to c89e4de Compare November 7, 2022 11:14
@ricardoV94 ricardoV94 merged commit 7608e30 into pymc-devs:main Nov 7, 2022
@ricardoV94 ricardoV94 deleted the categorical_multinomial_check_bugs branch November 8, 2022 10:17
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Problem with Categorical distribution with tensor probabilities

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