  It remains an open question how to determine the winner of an election when
voter preferences are incomplete or uncertain. One option is to assume some
probability space over the voting profile and select the Most Probable Winner
(MPW) -- the candidate or candidates with the best chance of winning. In this
paper, we propose an alternative winner interpretation, selecting the Most
Expected Winner (MEW) according to the expected performance of the candidates.
  We separate the uncertainty in voter preferences into the generation step and
the observation step, which gives rise to a unified voting profile combining
both incomplete and probabilistic voting profiles. We use this framework to
establish the theoretical hardness of \mew over incomplete voter preferences,
and then identify a collection of tractable cases for a variety of voting
profiles, including those based on the popular Repeated Insertion Model (RIM)
and its special case, the Mallows model. We develop solvers customized for
various voter preference types to quantify the candidate performance for the
individual voters, and propose a pruning strategy that optimizes computation.
The performance of the proposed solvers and pruning strategy is evaluated
extensively on real and synthetic benchmarks, showing that our methods are
practical.
