  For the purpose of minimizing the number of sample model evaluations, we
propose and study algorithms that utilize (sequential) versions of
likelihood-to-evidence ratio neural estimation.We apply our algorithms to a
supersymmetric interpretation of the anomalous muon magnetic dipole moment in
the context of a phenomenological minimal supersymmetric extension of the
standard model, and recover non-trivial models in an experimentally-constrained
theory space. Finally we summarize further potential possible uses of these
algorithms in future studies.
