  Multiple importance sampling (MIS) methods use a set of proposal
distributions from which samples are drawn. Each sample is then assigned an
importance weight that can be obtained according to different strategies. This
work is motivated by the trade-off between variance reduction and computational
complexity of the different approaches (classical vs. deterministic mixture)
available for the weight calculation. A new method that achieves an efficient
compromise between both factors is introduced in this paper. It is based on
forming a partition of the set of proposal distributions and computing the
weights accordingly. Computer simulations show the excellent performance of the
associated \mbox{\emph{partial deterministic mixture} MIS estimator.
