  Bayesian models of legal arguments generally aim to produce a single
integrated model, combining each of the legal arguments under consideration.
This combined approach implicitly assumes that variables and their
relationships can be represented without any contradiction or misalignment, and
in a way that makes sense with respect to the competing argument narratives.
This paper describes a novel approach to compare and 'average' Bayesian models
of legal arguments that have been built independently and with no attempt to
make them consistent in terms of variables, causal assumptions or
parametrisation. The approach involves assessing whether competing models of
legal arguments are explained or predict facts uncovered before or during the
trial process. Those models that are more heavily disconfirmed by the facts are
given lower weight, as model plausibility measures, in the Bayesian model
comparison and averaging framework adopted. In this way a plurality of
arguments is allowed yet a single judgement based on all arguments is possible
and rational.
