Looking for plausibility
  In the interpretation of experimental data, one is actually looking for
plausible explanations. We look for a measure of plausibility, with which we
can compare different possible explanations, and which can be combined when
there are different sets of data. This is contrasted to the conventional
measure for probabilities as well as to the proposed measure of possibilities.
We define what characteristics this measure of plausibility should have.
  In getting to the conception of this measure, we explore the relation of
plausibility to abductive reasoning, and to Bayesian probabilities. We also
compare with the Dempster-Schaefer theory of evidence, which also has its own
definition for plausibility. Abduction can be associated with biconditionality
in inference rules, and this provides a platform to relate to the
Collins-Michalski theory of plausibility. Finally, using a formalism for wiring
logic onto Hopfield neural networks, we ask if this is relevant in obtaining
this measure.
