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Fixes the difference b/w logpt and logp_nojac #3398

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Fixes the difference b/w logpt and logp_nojac #3398

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8 changes: 6 additions & 2 deletions pymc3/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -269,7 +269,9 @@ def logpt(self):

@property
def logp_nojact(self):
"""Theano scalar of log-probability, excluding jacobian terms."""
"""Theano scalar of log-probability of the model but without the jacobian
if transformed Random Variable is presented.
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@junpenglao junpenglao Mar 21, 2019

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Suggested change
if transformed Random Variable is presented.
if transformed Random Variable is presented.
Note that If there is no transformed variable in the model, logp_nojact
will be the same as logpt as there is no need for Jacobian correction.

"""
if getattr(self, 'total_size', None) is not None:
logp = tt.sum(self.logp_nojac_unscaledt) * self.scaling
else:
Expand Down Expand Up @@ -734,7 +736,9 @@ def logpt(self):

@property
def logp_nojact(self):
"""Theano scalar of log-probability of the model"""
"""Theano scalar of log-probability of the model but without the jacobian
if transformed Random Variable is presented.
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@lucianopaz lucianopaz Mar 9, 2019

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Does this mean that the Jacobian will be present if there are no transformed random variables? That is how I understand the sentence.

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@junpenglao junpenglao Mar 9, 2019

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If there are no transformed variables there is no need for Jacobian correction, hence logp_nojact will be the same as logpt
This should be rephrased to make it clearer indeed.

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@junpenglao junpenglao Mar 21, 2019

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Suggested change
if transformed Random Variable is presented.
if transformed Random Variable is presented.
Note that If there is no transformed variable in the model, logp_nojact
will be the same as logpt as there is no need for Jacobian correction.

"""
with self:
factors = [var.logp_nojact for var in self.basic_RVs] + self.potentials
logp = tt.sum([tt.sum(factor) for factor in factors])
Expand Down