  Many hard problems in the computational sciences are equivalent to counting
the leaves of a decision tree, or, more generally, summing a cost function over
the nodes. These problems include calculating the permanent of a matrix,
finding the volume of a convex polyhedron, and counting the number of linear
extensions of a partially ordered set. Many approximation algorithms exist to
estimate such sums. One of the most recent is Stochastic Enumeration (SE),
introduced in 2013 by Rubinstein. In 2015, Vaisman and Kroese provided a
rigorous analysis of the variance of SE, and showed that SE can be extended to
a fully polynomial randomized approximation scheme for certain cost functions
on random trees. We present an algorithm that incorporates an importance
function into SE, and provide theoretical analysis of its efficacy. We also
present the results of numerical experiments to measure the variance of an
application of the algorithm to the problem of counting linear extensions of a
poset, and show that introducing importance sampling results in a significant
reduction of variance as compared to the original version of SE.
