  Irrelevance-based partial MAPs are useful constructs for domain-independent
explanation using belief networks. We look at two definitions for such partial
MAPs, and prove important properties that are useful in designing algorithms
for computing them effectively. We make use of these properties in modifying
our standard MAP best-first algorithm, so as to handle irrelevance-based
partial MAPs.
