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Updated pymc.GaussianRandomWalk docstring #5492

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merged 2 commits into from
Feb 20, 2022
Merged

Updated pymc.GaussianRandomWalk docstring #5492

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e5e794b
Updated pymc.GaussianRandomWalk docstring
GallowayJ Feb 19, 2022
13649d0
Addressed review comments to changes to GWR docstring
GallowayJ Feb 19, 2022
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24 changes: 12 additions & 12 deletions pymc/distributions/timeseries.py
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Original file line number Diff line number Diff line change
Expand Up @@ -185,19 +185,19 @@ class GaussianRandomWalk(distribution.Continuous):

Parameters
----------
mu: tensor
mu : TensorVariable, optional
innovation drift, defaults to 0.0
For vector valued mu, first dimension must match shape of the random walk, and
For vector valued `mu`, first dimension must match shape of the random walk, and
the first element will be discarded (since there is no innovation in the first timestep)
sigma: tensor
sigma > 0, innovation standard deviation (only required if tau is not specified)
For vector valued sigma, first dimension must match shape of the random walk, and
sigma : TensorVariable, optional
`sigma` > 0, innovation standard deviation (only required if `tau` is not specified)
For vector valued `sigma`, first dimension must match shape of the random walk, and
the first element will be discarded (since there is no innovation in the first timestep)
tau: tensor
tau > 0, innovation precision (only required if sigma is not specified)
For vector valued tau, first dimension must match shape of the random walk, and
tau : TensorVariable, optional
`tau` > 0, innovation precision (only required if `sigma` is not specified)
For vector valued `tau`, first dimension must match shape of the random walk, and
the first element will be discarded (since there is no innovation in the first timestep)
init: distribution
init : distribution, optional
distribution for initial value (Defaults to Flat())
"""

Expand Down Expand Up @@ -230,7 +230,7 @@ def logp(self, x):

Parameters
----------
x: numeric
x : numeric
Value for which log-probability is calculated.

Returns
Expand All @@ -250,10 +250,10 @@ def random(self, point=None, size=None):

Parameters
----------
point: dict, optional
Point : dict, optional
Dict of variable values on which random values are to be
conditioned (uses default point if not specified).
size: int, optional
size : int, optional
Desired size of random sample (returns one sample if not
specified).

Expand Down