Queueing Systems

, Volume 10, Issue 1, pp 5–87

The Fourier-series method for inverting transforms of probability distributions

  • Joseph Abate
  • Ward Whitt

DOI: 10.1007/BF01158520

Cite this article as:
Abate, J. & Whitt, W. Queueing Syst (1992) 10: 5. doi:10.1007/BF01158520


This paper reviews the Fourier-series method for calculating cumulative distribution functions (cdf's) and probability mass functions (pmf's) by numerically inverting characteristic functions, Laplace transforms and generating functions. Some variants of the Fourier-series method are remarkably easy to use, requiring programs of less than fifty lines. The Fourier-series method can be interpreted as numerically integrating a standard inversion integral by means of the trapezoidal rule. The same formula is obtained by using the Fourier series of an associated periodic function constructed by aliasing; this explains the name of the method. This Fourier analysis applies to the inversion problem because the Fourier coefficients are just values of the transform. The mathematical centerpiece of the Fourier-series method is the Poisson summation formula, which identifies the discretization error associated with the trapezoidal rule and thus helps bound it. The greatest difficulty is approximately calculating the infinite series obtained from the inversion integral. Within this framework, lattice cdf's can be calculated from generating functions by finite sums without truncation. For other cdf's, an appropriate truncation of the infinite series can be determined from the transform based on estimates or bounds. For Laplace transforms, the numerical integration can be made to produce a nearly alternating series, so that the convergence can be accelerated by techniques such as Euler summation. Alternatively, the cdf can be perturbed slightly by convolution smoothing or windowing to produce a truncation error bound independent of the original cdf. Although error bounds can be determined, an effective approach is to use two different methods without elaborate error analysis. For this purpose, we also describe two methods for inverting Laplace transforms based on the Post-Widder inversion formula. The overall procedure is illustrated by several queueing examples.


Computational probabilitynumerical inversion of transformscharacteristic functionsLaplace transformsgenerating functionsFourier transformscumulative distribution functionscalculating tail probabilitiesnumerical integrationFourier seriesPoisson summation formulathe Fourier-series methodthe Gaver-Stehfest method

Copyright information

© J.C. Baltzer A.G. Scientific Publishing Company 1992

Authors and Affiliations

  • Joseph Abate
    • 1
  • Ward Whitt
    • 2
  1. 1.RidgewoodUSA
  2. 2.AT&T Bell LaboratoriesMurray HillUSA