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Combining Opinions for Use in Bayesian Networks: A Measurement Error Approach

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  • A. Charisse Farr
  • Kerrie Mengersen
  • Fabrizio Ruggeri
  • Daniel Simpson
  • Paul Wu
  • Prasad Yarlagadda

Abstract

Bayesian networks (BNs) are graphical probabilistic models used for reasoning under uncertainty. These models are becoming increasingly popular in a range of fields including engineering, ecology, computational biology, medical diagnosis and forensics. In most of these cases, the BNs are quantified using information from experts or from users' opinions. While this quantification is straightforward for one expert, there is still debate about how to represent opinions from multiple experts in a BN. This paper proposes the use of a measurement error model to achieve this. The proposed model addresses the issues associated with current methods of combining opinions such as the absence of a coherent probability model, the loss of the conditional independence structure of the BN and the provision of only a point estimate for the consensus. The proposed model is applied to a subnetwork (the three final nodes) of a larger BN about wayfinding in airports. It is shown that the approach performs well than do existing methods of combining opinions.

Suggested Citation

  • A. Charisse Farr & Kerrie Mengersen & Fabrizio Ruggeri & Daniel Simpson & Paul Wu & Prasad Yarlagadda, 2020. "Combining Opinions for Use in Bayesian Networks: A Measurement Error Approach," International Statistical Review, International Statistical Institute, vol. 88(2), pages 335-353, August.
    • Handle: RePEc:bla:istatr:v:88:y:2020:i:2:p:335-353
    DOI: 10.1111/insr.12350
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    References listed on IDEAS

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