A direct inversion method for non-uniform quasi-random point sequences
-
Colas Schretter
and
Harald Niederreiter
Abstract.
The inversion method is an effective approach for transforming uniform random points according to a given probability density function. In two dimensions, horizontal and vertical displacements are computed successively using a marginal and then all conditional density functions. When quasi-random low-discrepancy points are provided as input, spurious artifacts might appear if the density function is not separable. Therefore, this paper relies on combining intrinsic properties of the golden ratio sequence and the Hilbert space filling curve for generating non-uniform point sequences using a single step inversion method. Experiments show that this approach improves efficiency while avoiding artifacts for general discrete probability density functions.
© 2013 by Walter de Gruyter Berlin Boston
Articles in the same Issue
- Masthead
- A direct inversion method for non-uniform quasi-random point sequences
- A parallel algorithm for solving BSDEs
- Exact simulation of one-dimensional stochastic differential equations involving the local time at zero of the unknown process
- Erratum: Fast simulation of Gaussian random fields [Monte Carlo Methods Appl. 17 (2011), 195–214]
Articles in the same Issue
- Masthead
- A direct inversion method for non-uniform quasi-random point sequences
- A parallel algorithm for solving BSDEs
- Exact simulation of one-dimensional stochastic differential equations involving the local time at zero of the unknown process
- Erratum: Fast simulation of Gaussian random fields [Monte Carlo Methods Appl. 17 (2011), 195–214]
