Browsing by Author "Joe, Stephen"

Now showing items 1-5 of 10

  • Component-by-component construction of good intermediate-rank lattice rules

    Kuo, Frances Y.; Joe, Stephen (Society for Industrial and Applied Mathematics, 2003)
    It is known that the generating vector of a rank-1 lattice rule can be constructed component-by-component to achieve strong tractability error bounds in both weighted Korobov spaces and weighted Sobolev spaces. Since the ...
  • Constructing Sobol' sequences with better two-dimensional projections

    Joe, Stephen; Kuo, Frances Y. (Society for Industrial and Applied Mathematics, 2008-08)
    Direction numbers for generating Sobol' sequences that satisfy the so-called Property A in up to 1111 dimensions have previously been given in Joe and Kuo [ACM Trans. Math. Software, 29 (2003), pp. 49–57]. However, these ...
  • Construction of Good Rank-1 Lattice Rules Based on the Weighted Star Discrepancy

    Joe, Stephen (Springer, Berlin, 2006)
    The ‘goodness’ of a set of quadrature points in [0, 1]d may be measured by the weighted star discrepancy. If the weights for the weighted star discrepancy are summable, then we show that for n prime there exist n-point ...
  • Determination of the rank of an integration lattice

    Joe, Stephen; Lyness, J.N. (Springer Netherlands, 2006)
    The continuing and widespread use of lattice rules for high-dimensional numerical quadrature is driving the development of a rich and detailed theory. Part of this theory is devoted to computer searches for rules, appropriate ...
  • Good intermediate-rank lattice rules based on the weighted star discrepancy

    Sinescu, Vasile; Joe, Stephen (World Scientific, 2006)
    We study the problem of constructing good intermediate-rank lattice rules in the sense of having a low weighted star discrepancy. The intermediate-rank rules considered here are obtained by “copying” rank-1 lattice rules. ...

Stephen Joe has 6 co-authors in Research Commons.

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  • Computational Bayesian inference using low discrepancy sequences

    Brown, Paul Tamataatoi (The University of Waikato, 2019)
    The Integrated Nested Laplace Approximation (INLA) provides fast and accurate Bayesian inference for complex hierarchical models. For INLA, and other deterministic methods, the hyperparameter space is explored and points ...