  Statistically resolving the underlying haplotype pair for a genotype
measurement is an important intermediate step in gene mapping studies, and has
received much attention recently. Consequently, a variety of methods for this
problem have been developed. Different methods employ different statistical
models, and thus implicitly encode different assumptions about the nature of
the underlying haplotype structure. Depending on the population sample in
question, their relative performance can vary greatly, and it is unclear which
method to choose for a particular sample. Instead of choosing a single method,
we explore combining predictions returned by different methods in a principled
way, and thereby circumvent the problem of method selection.
  We propose several techniques for combining haplotype reconstructions and
analyze their computational properties. In an experimental study on real-world
haplotype data we show that such techniques can provide more accurate and
robust reconstructions, and are useful for outlier detection. Typically, the
combined prediction is at least as accurate as or even more accurate than the
best individual method, effectively circumventing the method selection problem.
