Approximating the permanent via importance sampling with application to the dimer covering problem
Journal of Computational Physics
Estimating the Work in Integer Partitioning
Computing in Science and Engineering
Multidimensional Integration: Partition and Conquer
Computing in Science and Engineering
Multidimensional numerical integration for robust design optimization
ACM-SE 45 Proceedings of the 45th annual southeast regional conference
Monte Carlo Minimization and Counting: One, Two, ..., Too Many
Computing in Science and Engineering
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Importance sampling is an underappreciated Monte Carlo technique. The authors have used it with great success. They share their insights. Importance sampling is designed to reduce the variance of the estimators for a given-size sample