Reliable Computing

, Volume 4, Issue 2, pp 147–165 | Cite as

Bounding the Results of Arithmetic Operations on Random Variables of Unknown Dependency Using Intervals

  • Daniel Berleant
  • Chaim Goodman-Strauss


Many real problems involve calculations on random variables, yet precise details about the correlations or other dependency relationships among those variables are often unknown.

For example consider analyzing the cancer risk associated with an environmental contaminant. The dependency of an individual's cumulative exposure on the less useful (but more obtainable) current exposure level will be uncertain. In this and many other cases, data points from which to derive such dependencies are sparse, and obtaining additional data is prohibitively expensive or difficult. Thus manipulating variables whose dependencies are unspecified is a problem of significance.

This paper describes a new approach to bounding the results of arithmetic operations on random variables when the dependency relationship between the variables is unspecified. The bounds enclose the space in which the result's distribution function can be.


Mathematical Modeling Distribution Function Cancer Risk Computational Mathematic Additional Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Kluwer Academic Publishers 1998

Authors and Affiliations

  • Daniel Berleant
    • 1
  • Chaim Goodman-Strauss
    • 1
  1. 1.University of ArkansasFayettevilleUSA, e-mail

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