@@ -198,8 +198,8 @@ def __new__(
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total_size = None ,
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transform = UNSET ,
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** kwargs ,
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- ) -> RandomVariable :
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- """Adds a RandomVariable corresponding to a PyMC distribution to the current model.
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+ ) -> TensorVariable :
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+ """Adds a tensor variable corresponding to a PyMC distribution to the current model.
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Note that all remaining kwargs must be compatible with ``.dist()``
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@@ -231,8 +231,8 @@ def __new__(
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Returns
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-------
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- rv : RandomVariable
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- The created RV , registered in the Model.
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+ rv : TensorVariable
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+ The created random variable tensor , registered in the Model.
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"""
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try :
@@ -296,8 +296,8 @@ def dist(
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* ,
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shape : Optional [Shape ] = None ,
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** kwargs ,
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- ) -> RandomVariable :
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- """Creates a RandomVariable corresponding to the `cls` distribution.
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+ ) -> TensorVariable :
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+ """Creates a tensor variable corresponding to the `cls` distribution.
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Parameters
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----------
@@ -314,8 +314,8 @@ def dist(
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Returns
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-------
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- rv : RandomVariable
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- The created RV .
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+ rv : TensorVariable
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+ The created random variable tensor .
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"""
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if "testval" in kwargs :
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kwargs .pop ("testval" )
@@ -653,8 +653,8 @@ def __new__(
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name : str
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dist_params : Tuple
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A sequence of the distribution's parameter. These will be converted into
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- Aesara tensors internally. These parameters could be other ``RandomVariable ``
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- instances.
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+ Aesara tensors internally. These parameters could be other ``TensorVariable ``
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+ instances created from , optionally created via ``RandomVariable`` ``Op``s .
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logp : Optional[Callable]
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A callable that calculates the log density of some given observed ``value``
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conditioned on certain distribution parameter values. It must have the
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