General Sequential Sampling Techniques for Monte Carlo Simulations: Part I—Matrix Problems

  • Jerome Spanier
  • Liming Li
Part of the Lecture Notes in Statistics book series (LNS, volume 127)


We study sequential sampling methods based principally on ideas of Halton. Such methods are designed to build information drawn from early batches of random walk histories into the random walk process used to generate later histories in order to accelerate convergence. In previously published work, such methods have been applied within the pseudorandom, rather than the quasirandom, context and have been applied only to matrix problems. In this paper, more general sequential techniques are formulated in an abstract space, such as Banach space. The more general formulation enables applications to linear algebraic equations and to integral equations to be obtained as special cases through specification of the Banach space and the operator defined on it. In this initial paper we outline the ideas needed for consideration of the more general problem and exhibit greatly accelerated convergence for a simple matrix problem. In a companion paper in which similar ideas are applied to the more important class of integral equations, the need for quasirandom implementation is stressed.


Random Walk Importance Sampling Sequential Sampling Transport Problem Actual Error 
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Copyright information

© Springer Science+Business Media New York 1998

Authors and Affiliations

  • Jerome Spanier
    • 1
  • Liming Li
    • 1
  1. 1.Department of MathematicsThe Claremont Graduate SchoolClaremontUSA

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