Computer implementation of probability distribution quantile estimation

Xian Chuan Yu, Zhongyi Yuan, Chen Yu, Meng Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Generally, we get probability distribution quantile by looking through numerical tables, however, it is not only easy to make mistake, but also limited in precision, no more than 0.0001. And programming techniques up to now are either too restrictive to be applied to general cases, or too complicated to be implemented for practical use. Therefore, there is a need for robust procedures to estimate quantiles, which can be applied to relatively generic processes and easy to implement. The paper briefly discusses the algorithm and the implement of some familiar probability distribution quantiles, such as, standardized normal distribution, β distribution, X2 distribution, t distribution and F distribution. Especially, we use Newton dichotomy here to improve the precision, in the case of t and F distributions which is insufficient by approximate formulae only, because of the accumulated error. An experimental performance evaluation demonstrates the validity of these procedures to calculate probability distribution quantiles.

Original languageEnglish (US)
Title of host publication2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005
Pages2783-2788
Number of pages6
StatePublished - Dec 12 2005
EventInternational Conference on Machine Learning and Cybernetics, ICMLC 2005 - Guangzhou, China
Duration: Aug 18 2005Aug 21 2005

Publication series

Name2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005

Other

OtherInternational Conference on Machine Learning and Cybernetics, ICMLC 2005
CountryChina
CityGuangzhou
Period8/18/058/21/05

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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  • Cite this

    Yu, X. C., Yuan, Z., Yu, C., & Yang, M. (2005). Computer implementation of probability distribution quantile estimation. In 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005 (pp. 2783-2788). (2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005).