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2 changes: 1 addition & 1 deletion docs/conf.py
Original file line number Diff line number Diff line change
Expand Up @@ -203,7 +203,7 @@
# Bibliographic Dublin Core info.
epub_title = project

# The unique identifier of the text. This can be a ISBN number
# The unique identifier of the text. This can be an ISBN number
# or the project homepage.
#
# epub_identifier = ''
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2 changes: 1 addition & 1 deletion preliz/distributions/exgaussian.py
Original file line number Diff line number Diff line change
Expand Up @@ -130,7 +130,7 @@ def rvs(self, size=None, random_state=None):
)

def _fit_moments(self, mean, sigma):
# Just assume this is a approximately Gaussian
# Just assume this is approximately Gaussian
self._update(mean, sigma, 1e-4)

def _fit_mle(self, sample):
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4 changes: 2 additions & 2 deletions preliz/unidimensional/matching.py
Original file line number Diff line number Diff line change
Expand Up @@ -99,7 +99,7 @@ def match_moments(

# Initialize `to_dist` to a distribution matching the mean and standard deviation
# of `from_dist`. The ``_fit_moments`` method is correct for some distributions,
# but just an heuristic for others.
# but just a heuristic for others.
to_dist._fit_moments(from_dist.mean(), from_dist.std())

opt = optimize_moments(to_dist, moments, target_values, none_idx, fixed)
Expand Down Expand Up @@ -212,7 +212,7 @@ def match_quantiles(

# Initialize `to_dist` to a distribution matching the mean and standard deviation
# of `from_dist`. The ``_fit_moments`` method is correct for some distributions,
# but just an heuristic for others.
# but just a heuristic for others.
to_dist._fit_moments(from_dist.mean(), from_dist.std())

opt = optimize_quantiles(to_dist, quantiles, target_values, none_idx, fixed)
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