Monte Carlo pp 255-334 | Cite as

Increasing Efficiency

  • George S. Fishman
Part of the Springer Series in Operations Research book series (ORFE)


Section 2.7 broadened the concept of a Monte Carlo experiment to the general problem of evaluating the Lebesgue-Stieltjes integral
$$\zeta = \int_\xi {k\left( z \right)} dF\left( z \right),$$
where z = (z 1,..., z m ), {F(z)} is a joint d.f. on the m-dimensional region and {k(z)} denotes a weighting kernel defined on Also, the introduction to Ch. 3 indicates that alternative d.f.s and kernels may exist which satisfy expression (1). We call each {F(z), k(z);z } satisfying expression (1) a sampling plan, since each provides a basis for generating data which can be used to estimate ζ unbiasedly. Whereas Ch. 3 describes how to generate data efficiently from a particular {F(z), z } once a sampling plan is chosen, the present chapter studies the relative desirability of alternative sampling plans from the viewpoint of computational efficiency.


Sampling Plan Unbiased Estimator Importance Sampling Variance Reduction Percent Confidence Interval 
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Copyright information

© Springer Science+Business Media New York 1996

Authors and Affiliations

  • George S. Fishman
    • 1
  1. 1.Department of Operations ResearchUniversity of North CarolinaChapel HillUSA

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