Confidence intervals (CI) are used to gauge the accuracy of bit error rate (BER) estimates produced by Monte Carlo (MC) simulations. This work attempts to objectively evaluate the performance of Important Sampling (IS) simulations by applying the same statistical analysis tool. While it is not possible to evaluate the minimum size CI for arbitrary IS estimates, it is possible to over-bound the interval using a technique called conservative confidence interval (CCI) estimation. This bounding procedure is applied to a simple IS biasing technique. While the IS estimate may be superior to the MC estimate, the CCI fails to support this claim. Since there has been little previous work published in the area of CI of IS estimates, this document is offered as a starting point. Hopefully others will be able to develop tighter bounds for the CI of IS estimates
K. L. Kosbar and T. F. Chang, "Conservative Confidence Intervals of Importance Sampling Estimates," MILCOM '92 Conference Record - Communications - Fusing Command, Control and Intelligence, pp. 318-323, Institute of Electrical and Electronics Engineers (IEEE), Oct 1992.
The definitive version is available at https://doi.org/10.1109/MILCOM.1992.244065
1992 IEEE Military Communications Conference, MILCOM '92 (1992: Oct. 11-14, San Diego, CA)
Electrical and Computer Engineering
Keywords and Phrases
Monte Carlo Methods; Monte Carlo Simulations; Bit Error Rate Estimates; Conservative Confidence Interval Estimation; Error Statistics; Importance Sampling Estimates; Performance; Signal Processing; Statistical Analysis
International Standard Book Number (ISBN)
Article - Conference proceedings
© 1992 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.