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Update pymc.TruncatedNormal docstring #5546

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Merged
OriolAbril merged 7 commits into from
Mar 12, 2022
Merged

Update pymc.TruncatedNormal docstring #5546

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df1850c
fixed TruncateNormal docsting
purna135 Mar 4, 2022
4f6fff4
Merge remote-tracking branch 'upstream/main' into doc_continuous_Trun…
purna135 Mar 6, 2022
4a12878
fixed TruncateNormal docsting
purna135 Mar 6, 2022
c972983
removed sd from dist
purna135 Mar 10, 2022
016b88e
Merge remote-tracking branch 'upstream/main' into doc_continuous_Trun…
purna135 Mar 10, 2022
d36d34c
undo the sd change
purna135 Mar 12, 2022
fbf1190
Merge remote-tracking branch 'upstream/main' into doc_continuous_Trun…
purna135 Mar 12, 2022
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19 changes: 11 additions & 8 deletions pymc/distributions/continuous.py
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Original file line number Diff line number Diff line change
Expand Up @@ -645,6 +645,7 @@ class TruncatedNormal(BoundedContinuous):


.. plot::
:context: close-figs

import matplotlib.pyplot as plt
import numpy as np
Expand Down Expand Up @@ -673,13 +674,16 @@ class TruncatedNormal(BoundedContinuous):

Parameters
----------
mu: float
mu : tensor_like of float, default 0
Mean.
sigma: float
Standard deviation (sigma > 0).
lower: float (optional)
sigma : tensor_like of float, optional
Standard deviation (sigma > 0) (only required if tau is not specified).
Defaults to 1 if neither sigma nor tau is specified.
tau : tensor_like of float, optional
Precision (tau > 0) (only required if sigma is not specified).
lower : tensor_like of float, default - numpy.inf
Left bound.
upper: float (optional)
upper : tensor_like of float, default numpy.inf
Right bound.

Examples
Expand Down Expand Up @@ -709,7 +713,6 @@ def dist(
sd: Optional[DIST_PARAMETER_TYPES] = None,
lower: Optional[DIST_PARAMETER_TYPES] = None,
upper: Optional[DIST_PARAMETER_TYPES] = None,
transform: str = "auto",
*args,
**kwargs,
) -> RandomVariable:
Expand Down Expand Up @@ -762,9 +765,9 @@ def logp(

Parameters
----------
value: numeric
value : tensor_like of float
Value(s) for which log-probability is calculated. If the log probabilities for multiple
values are desired the values must be provided in a numpy array or Aesara tensor
values are desired the values must be provided in a numpy array or Aesara tensor.

Returns
-------
Expand Down