  The study of a machine learning problem is in many ways is difficult to
separate from the study of the loss function being used. One avenue of inquiry
has been to look at these loss functions in terms of their properties as
scoring rules via the proper-composite representation, in which predictions are
mapped to probability distributions which are then scored via a scoring rule.
However, recent research so far has primarily been concerned with analysing the
(typically) finite-dimensional conditional risk problem on the output space,
leaving aside the larger total risk minimisation. We generalise a number of
these results to an infinite dimensional setting and in doing so we are able to
exploit the familial resemblance of density and conditional density estimation
to provide a simple characterisation of the canonical link.
