message: Add note to bijector_impl.py explaining that `log_det_jacobian` is `log(*abs*(det(Jacobian)))`.
diff: @@ -160,13 +160,20 @@ class Bijector(object):
  3. `log_det_jacobian(x)`
  - "The log of the determinant of the matrix of all first-order partial
  - derivatives of the inverse function."
  + "The log of the absolute value of the determinant of the matrix of all
  + first-order partial derivatives of the inverse function."
  Useful for inverting a transformation to compute one probability in terms
  of another. Geometrically, the Jacobian determinant is the volume of the
  transformation and is used to scale the probability.
  + We take the absolute value of the determinant before log to avoid NaN
  + values. Geometrically, a negative determinant corresponds to an
  + orientation-reversing transformation. It is ok for us to discard the sign
  + of the determinant because we only integrate everywhere-nonnegative
  + functions (probability densities) and the correct orientation is always the
  + one that produces a nonnegative integrand.
  +
  By convention, transformations of random variables are named in terms of the
  forward transformation. The forward transformation creates samples, the
  inverse is useful for computing probabilities.
  