original_docstring: Return the logp, but do not include a jacobian term for transforms.
  
          If we use different parametrizations for the same distribution, we
          need to add the determinant of the jacobian of the transformation
          to make sure the densities still describe the same distribution.
          However, MAP estimates are not invariant with respect to the
          parametrization, we need to exclude the jacobian terms in this case.
  
          This function should be overwritten in base classes for transformed
          distributions.
          
docstring: Return the logp, but do not include a jacobian term for transforms.
  If we use different parametrizations for the same distribution, we
  need to add the determinant of the jacobian of the transformation
  to make sure the densities still describe the same distribution.
  However, MAP estimates are not invariant with respect to the
  parametrization, we need to exclude the jacobian terms in this case.
  
  This function should be overwritten in base classes for transformed
  distributions.
docstring_tokens: Return the logp but do not include a jacobian term for transforms . If we use different parametrizations for the same distribution we need to add the determinant of the jacobian of the transformation to make sure the densities still describe the same distribution . However MAP estimates are not invariant with respect to the parametrization we need to exclude the jacobian terms in this case . This function should be overwritten in base classes for transformed distributions .
code: def logp_nojac(self, *args, **kwargs):
          """Return the logp, but do not include a jacobian term for transforms.
  
          If we use different parametrizations for the same distribution, we
          need to add the determinant of the jacobian of the transformation
          to make sure the densities still describe the same distribution.
          However, MAP estimates are not invariant with respect to the
          parametrization, we need to exclude the jacobian terms in this case.
  
          This function should be overwritten in base classes for transformed
          distributions.
          """
          return self.logp(*args, **kwargs)
code_tokens: def logp_nojac ( self , * args , ** kwargs ) : return self . logp ( * args , ** kwargs )
short_docstring: Return the logp, but do not include a jacobian term for transforms.
short_docstring_tokens: Return the logp but do not include a jacobian term for transforms .
comment: """Return the logp, but do not include a jacobian term for transforms.
  
          If we use different parametrizations for the same distribution, we
          need to add the determinant of the jacobian of the transformation
          to make sure the densities still describe the same distribution.
          However, MAP estimates are not invariant with respect to the
          parametrization, we need to exclude the jacobian terms in this case.
  
          This function should be overwritten in base classes for transformed
          distributions.
          """