Quantifying uncertainty in the estimation of probability distributions

  • Received: 01 December 2007 Accepted: 29 June 2018 Published: 01 October 2008
  • MSC : 35L60,62E20,62F25,62G15,65M32,92D25

  • We consider ordinary least squares parameter estimation problems where the unknown parameters to be estimated are probability distributions. A computational framework for quantification of uncertainty (e.g., standard errors) associated with the estimated parameters is given and sample numerical findings are presented.

    Citation: H.T. Banks, Jimena L. Davis. Quantifying uncertainty in the estimation of probability distributions[J]. Mathematical Biosciences and Engineering, 2008, 5(4): 647-667. doi: 10.3934/mbe.2008.5.647

    Related Papers:

  • We consider ordinary least squares parameter estimation problems where the unknown parameters to be estimated are probability distributions. A computational framework for quantification of uncertainty (e.g., standard errors) associated with the estimated parameters is given and sample numerical findings are presented.


    加载中
  • Reader Comments
  • © 2008 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1618) PDF downloads(479) Cited by(8)

Article outline

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog