Black-box algorithms for sampling from continuous distributions

Authors:
Josef Leydold;Wolfgang Hörmann
Affiliations:
Vienna University of Economics and Business Administration, Vienna, Austria;Vienna University of Economics and Business Administration, Vienna, Austria
Venue:
Proceedings of the 38th conference on Winter simulation
Year:
2006

Citing 6
Cited 0

A guide to simulation (2nd ed.)

A guide to simulation (2nd ed.)
Noninverse correlation induction: guidelines for algorithm development

Journal of Computational and Applied Mathematics - Random numbers and simulation
A rejection technique for sampling from T-concave distributions

ACM Transactions on Mathematical Software (TOMS)
Numerical computing with IEEE floating point arithmetic

Numerical computing with IEEE floating point arithmetic
An Object-Oriented Random-Number Package with Many Long Streams and Substreams

Operations Research
Continuous random variate generation by fast numerical inversion

ACM Transactions on Modeling and Computer Simulation (TOMACS)

Quantified Score

Hi-index	0.00

Visualization

Abstract

For generating non-uniform random variates, black-box algorithms are powerful tools that allow drawing samples from large classes of distributions. We give an overview of the design principles of such methods and show that they have advantages compared to specialized algorithms even for standard distributions, e.g., the marginal generation times are fast and depend mainly on the chosen method and not on the distribution. Moreover these methods are suitable for specialized tasks like sampling from truncated distributions and variance reduction techniques. We also present a library called UNU.RAN that provides an interface to a portable implementation of such methods.