Extending the alias Monte Carlo sampling method to general distributions
- Lawrence Livermore National Lab., CA (USA)
- California Polytechnic State Univ., San Luis Obispo, CA (USA)
The alias method is a Monte Carlo sampling technique that offers significant advantages over more traditional methods. It equals the accuracy of table lookup and the speed of equal probable bins. The original formulation of this method sampled from discrete distributions and was easily extended to histogram distributions. We have extended the method further to applications more germane to Monte Carlo particle transport codes: continuous distributions. This paper presents the alias method as originally derived and our extensions to simple continuous distributions represented by piecewise linear functions. We also present a method to interpolate accurately between distributions tabulated at points other than the point of interest. We present timing studies that demonstrate the method's increased efficiency over table lookup and show further speedup achieved through vectorization. 6 refs., 12 figs., 2 tabs.
- Research Organization:
- Lawrence Livermore National Lab., CA (USA)
- Sponsoring Organization:
- DOE/DP
- DOE Contract Number:
- W-7405-ENG-48
- OSTI ID:
- 6023539
- Report Number(s):
- UCRL-JC-104791; CONF-910414-16; ON: DE91007619; TRN: 91-004815
- Resource Relation:
- Conference: International topical meeting on advances in mathematics, computations and reactor physics, Pittsburgh, PA (USA), 28 Apr - 2 May 1991
- Country of Publication:
- United States
- Language:
- English
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