Sampling from a Discrete Distribution while Preserving Monotonicity

“…When the state space is discrete and large, the inverse transformation is slow if implemented crudely. Index tables can speed execution time; see Fishman and Moore (1984).…”

Section: A R K O V C H a I N Smentioning

confidence: 99%

Inverse-transformation algorithms for some common stochastic processes

Schmeiser

Song²

1989

Proceedings of the 21st Conference on Winter Simulation - WSC '89

View full text Add to dashboard Cite

Realizations from common stochastic processes are often used by simulation-methodology researchers in Monte Carlo performance evaluation of new and existing methods for output analysis, variance reduction, and optimization. Typically realizations can be obtained easily from either the definition or simple properties of the process. We discuss using the inverse of the distribution function for generating realizations from some of these processes. The inverse transformation always possesses the advantage of correlation induction, useful for variance reduction. We consider the discrete-time processes ARMA, EAR, M/M/1-QT (time in queue), and M/M/I-ST (time in system, the sojourn time), and Markov chains. The inversetransformation algorithms are sometimes slower (e.g., ARMA, M/M/I-ST), sometimes faster (e.g., M/M/1-QT), and often about the same speed as the usual algorithm. Some Fortran implementations are provided.

show abstract

Generation of Random Variables, Computer

Kemp¹

2005

Encyclopedia of Statistical Sciences

View full text Add to dashboard Cite

Sampling from a Discrete Distribution while Preserving Monotonicity

Cited by 16 publications

References 0 publications

Monte Carlo

Monte Carlo

Inverse-transformation algorithms for some common stochastic processes

Generation of Random Variables, Computer

Contact Info

Product

Resources

About