  A sequential importance sampling algorithm is developed for the distribution
that results when a matrix of independent, but not identically distributed,
Bernoulli random variables is conditioned on a given sequence of row and column
sums. This conditional distribution arises in a variety of applications and
includes as a special case the uniform distribution over zero-one tables with
specified margins. The algorithm uses dynamic programming to combine hard
margin constraints, combinatorial approximations, and additional non-uniform
weighting in a principled way to give state-of-the-art results.
