  A probability model is underdetermined when there is no rational reason to
assign a particular infinitesimal value as the probability of single events.
Pruss claims that hyperreal probabilities are underdetermined. The claim is
based upon external hyperreal-valued measures. We show that internal
hyperfinite measures are not underdetermined. The importance of internality
stems from the fact that Robinson's transfer principle only applies to internal
entities. We also evaluate the claim that transferless ordered fields
(surreals, Levi-Civita field, Laurent series) may have advantages over
hyperreals in probabilistic modeling. We show that probabilities developed over
such fields are less expressive than hyperreal probabilities.
