  In recent years, sequential importance sampling (SIS) has been well developed
for sampling contingency tables with linear constraints. In this paper, we
apply SIS procedure to 2-dimensional Ising models, which give observations of
0-1 tables and include both linear and quadratic constraints. We show how to
compute bounds for specific cells by solving linear programming (LP) problems
over cut polytopes to reduce rejections. The computational results, which
includes both simulations and real data analysis, suggest that our method
performs very well for sparse tables and when the 1's are spread out: the
computational times are short, the acceptance rates are high, and if proper
tests are used then in most cases our conclusions are theoretically reasonable.
