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Implement logcdf method for discrete distributions #4387

Merged
merged 9 commits into from
Dec 31, 2020
1 change: 1 addition & 0 deletions RELEASE-NOTES.md
Original file line number Diff line number Diff line change
Expand Up @@ -19,6 +19,7 @@ It also brings some dreadfully awaited fixes, so be sure to go through the chang
- Option to set `check_bounds=False` when instantiating `pymc3.Model()`. This turns off bounds checks that ensure that input parameters of distributions are valid. For correctly specified models, this is unneccessary as all parameters get automatically transformed so that all values are valid. Turning this off should lead to faster sampling (see [#4377](https://github.com/pymc-devs/pymc3/pull/4377)).
- `OrderedProbit` distribution added (see [#4232](https://github.com/pymc-devs/pymc3/pull/4232)).
- `plot_posterior_predictive_glm` now works with `arviz.InferenceData` as well (see [#4234](https://github.com/pymc-devs/pymc3/pull/4234))
- Add `logcdf` method to all univariate discrete distributions (see [#4387](https://github.com/pymc-devs/pymc3/pull/4387)).

### Maintenance
- Fixed bug whereby partial traces returns after keyboard interrupt during parallel sampling had fewer draws than would've been available [#4318](https://github.com/pymc-devs/pymc3/pull/4318)
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