Title :
Random Variate Generation for Monte Carlo Experiments
Author :
Leemis, Lawrence ; Schmeiser, Bruce
Author_Institution :
The University of Oklahoma, School of Industrial Engineering; 202 West Boyd, Suite 124; Norman, Oklahoma USA.
fDate :
4/1/1985 12:00:00 AM
Abstract :
We discuss methods for generating observations from specified distributions, based on a taxonomy that emphasizes analogies between methods based on the probability-density and cumulative-distribution functions and methods based on the hazard rate and cumulative hazard functions. Four categories are identified: inversion methods, linear combination methods, majorizing methods and special properties. Examples are given of each.
Keywords :
Computational modeling; Computer simulation; Distributed computing; Distribution functions; Hazards; Monte Carlo methods; Probability; Random number generation; Random variables; Taxonomy; Competing risks; Hazard function; Random numbers; Simulation; Thinning;
Journal_Title :
Reliability, IEEE Transactions on
DOI :
10.1109/TR.1985.5221941
