Skip to main content

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 67))

  • 942 Accesses

Abstract

With increasing dependence on networks to conduct business, the issue of net-work reliability has never been more critical. In this paper, we discuss various methods for estimating the reliability of networks under various assumptions. We use several techniques depending on the information available on the network, including Bayesian inference and neural networks.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Chen, J. and Singpurwalla, N. D. (1994). The Notion of Composite Reliability and its Hierarchical Bayes Estimation. Journal of the American Statistical Association 91: 1474–1484.

    MathSciNet  Google Scholar 

  2. DeFinetti, B. (1972). Probability, Induction and Statistics John Wiley, New York.

    Google Scholar 

  3. DeFinetti, B. (1975). Theorem of Probability John Wiley, New York.

    Google Scholar 

  4. DeGroot, M. H. (1989). Probability and Statistics 2nd edn, Addison-Wesley, Reading, MA.

    Google Scholar 

  5. Freund, J. E. (1961). A Bivariate Extension of the Exponential Distribution. Journal of the American Statistical Association 56: 971–977.

    Article  MathSciNet  MATH  Google Scholar 

  6. Kong, C. W. and Singpurwalla, N. D. (2001). Neural Nets for Network Reliability. to appear

    Google Scholar 

  7. Lindley, D. V. and Singpurwalla, N. D. (2002). On Exchangeable, Causal and Cascading Failures. Statistical Science 17: 209–219.

    Article  MathSciNet  MATH  Google Scholar 

  8. Lynn, N., Singpurwalla, N. D. and Smith, A. F. M. (1998). Bayesian Assessment of Network Reliability. SIAM Review 40: 202–227.

    Article  MathSciNet  MATH  Google Scholar 

  9. McCulloch, W. S. and Pitts, W. (1943). A Logical Calculus of Ideas Imminent in Nervous Activity. Bulletin of Mathematical Biophysics 5:115–133.

    Article  MathSciNet  MATH  Google Scholar 

  10. Roberts, G. O. and Smith, A. F. M. (1993). Bayesian Methods via the Gibbs Sampler and Related Markov Chain Monte Carlo Methods. J. Roy. Statist. Soc. B 55: 3–23.

    MathSciNet  MATH  Google Scholar 

  11. Singpurwalla, N. D. and Swift, A. W. (2001). Network Reliability and Borel’s Paradox. The American Statistician 55: 213–218.

    Article  MathSciNet  MATH  Google Scholar 

  12. Swift, A. W. (2001). Stochastic Models of Cascading Failures. PhD thesis The George Washington University.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer Science+Business Media New York

About this chapter

Cite this chapter

Swift, A.W. (2004). Methods for Assessing Network Reliability. In: Soyer, R., Mazzuchi, T.A., Singpurwalla, N.D. (eds) Mathematical Reliability: An Expository Perspective. International Series in Operations Research & Management Science, vol 67. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-9021-1_3

Download citation

  • DOI: https://doi.org/10.1007/978-1-4419-9021-1_3

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-4760-6

  • Online ISBN: 978-1-4419-9021-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics