Modelling the reliability of search operations within the UK through Bayesian belief networks

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)
97 Downloads (Pure)


This paper uses a Bayesian belief networks (BBN) methodology to assess the reliability of search and rescue (SAR) operations within the UK coastguard (maritime rescue) coordination centers. This is an extension of earlier work, which investigated the rationale of the government's decision to close a number of coordination centers. The previous study made use of secondary data sources and employed a binary logistic regression methodology to support the analysis. This study focused on the collection of primary data through a structured elicitation process, which resulted in the construction of a BBN. The main findings of the study are that approaches such as logistic regression are complementary to BBN's. The former provided a more objective assessment of associations between variables but was restricted in the level of detail that could be explicitly expressed within the model due to lack of available data. The latter method provided a much more detailed model but the validity of the numeric assessments was more questionable. Each method can be used to inform and defend the development of the other. The paper describes in detail the elicitation process employed to construct the BBN and reflects on the potential for bias.
Original languageEnglish
Number of pages6
Publication statusPublished - 8 May 2006
EventInternational Conference on Availability, Reliability and Security 2006 - Vienna, Austria
Duration: 20 Apr 200622 Apr 2006


ConferenceInternational Conference on Availability, Reliability and Security 2006
Abbreviated titleARES 2006


  • search operations
  • coastguards
  • maritime rescue
  • UK
  • Bayesian
  • BBN
  • Bayesian Relief Networks

Fingerprint Dive into the research topics of 'Modelling the reliability of search operations within the UK through Bayesian belief networks'. Together they form a unique fingerprint.

Cite this