  The maximum-entropy sampling problem is a fundamental and challenging
combinatorial-optimization problem, with application in spatial statistics. It
asks to find a maximum-determinant order-$s$ principal submatrix of an
order-$n$ covariance matrix. Exact solution methods for this NP-hard problem
are based on a branch-and-bound framework. Many of the known upper bounds for
the optimal value are based on convex optimization. We present a methodology
for "mixing" these bounds to achieve better bounds.
