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Estimation of Error and Bias in Bayesian Monte Carlo Decision Analysis Using the Bootstrap

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  • Charles D. Linville
  • Benjamin F. Hobbs
  • Boddu N. Venkatesh

Abstract

Bayesian Monte Carlo (BMC) decision analysis adopts a sampling procedure to estimate likelihoods and distributions of outcomes, and then uses that information to calculate the expected performance of alternative strategies, the value of information, and the value of including uncertainty. These decision analysis outputs are therefore subject to sample error. The standard error of each estimate and its bias, if any, can be estimated by the bootstrap procedure. The bootstrap operates by resampling (with replacement) from the original BMC sample, and redoing the decision analysis. Repeating this procedure yields a distribution of decision analysis outputs. The bootstrap approach to estimating the effect of sample error upon BMC analysis is illustrated with a simple value‐of‐information calculation along with an analysis of a proposed control structure for Lake Erie. The examples show that the outputs of BMC decision analysis can have high levels of sample error and bias.

Suggested Citation

  • Charles D. Linville & Benjamin F. Hobbs & Boddu N. Venkatesh, 2001. "Estimation of Error and Bias in Bayesian Monte Carlo Decision Analysis Using the Bootstrap," Risk Analysis, John Wiley & Sons, vol. 21(1), pages 63-74, February.
    • Handle: RePEc:wly:riskan:v:21:y:2001:i:1:p:63-74
    DOI: 10.1111/0272-4332.211090
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    Cited by:

    1. Alan Brennan & Samer Kharroubi & Anthony O'Hagan & Jim Chilcott, 2007. "Calculating Partial Expected Value of Perfect Information via Monte Carlo Sampling Algorithms," Medical Decision Making, , vol. 27(4), pages 448-470, July.

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