Abstract
The possible sources of error in Monte Carlo (MC) simulations are errors in physical modeling, coding errors (bugs), statistical errors, and algorithmic errors. While most algorithmic errors lead to large statistical errors, subtle algorithmic errors that do not lead to statistical errors are at least theoretically possible. These sources of error are reviewed, with the emphasis on statistical errors. Methods for calculating the statistical error in two types of biasing MC simulation, i.e., 1) standard importance sampling and 2) multicanonical, are described. The former requires a priori knowledge of how to bias the simulation, while the latter does not. Examples are drawn from the work of the author and his colleagues on calculating the effects of polarization-mode dispersion in optical fiber communication systems. Potential pitfalls when MC simulation codes are not carefully validated and statistical errors are not carefully monitored are described. A proposal for “best practice” in which statistical errors are always presented in conjunction with MC simulations is made.
© 2006 IEEE
PDF Article
More Like This
Cited By
You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.
Contact your librarian or system administrator
or
Login to access Optica Member Subscription