摘要：Bayesian model averaging (BMA) is a way of taking
account of uncertainty about model form or assumptions
and propagating it through to inferences about
an unknown quantity of interest such as a population
parameter, a future observation, or the future payoff
or cost of a course of action. The BMA posterior distribution
of the quantity of interest is a weighted average
of its posterior distributions under each of the
models considered, where a model’s weight is equal
to the posterior probability that it is correct, given
that one of the models considered is correct.
