Testing plays a vital role in reducing uncertainty but is resource intensive and identifying the best design is a difficult process. During the development of a system there are a number of potential tests that can be performed with varying efficacy and resource requirements. In this paper we propose a Bayesian modelling process which takes the form of a Bayesian Belief Network (BBN) to determine test efficacy and permits programme managers to assess optimal trade-offs between uncertainty reduction and resources. Supporting inference from a full Bayesian model can be prohibitively expensive computationally so we utilise a Bayes linear approximation, known as a Bayes linear Bayes graphical model, to the inference.
|Publication status||Unpublished - Jul 2013|
|Event||8th International Conference on Mathematical Methods in Reliability (MMR2013) - Stellenboch, South Africa|
Duration: 1 Jul 2013 → 4 Jul 2013
|Conference||8th International Conference on Mathematical Methods in Reliability (MMR2013)|
|Period||1/07/13 → 4/07/13|
- Bayesian modelling
- Bayes graphical model
- Bayesian belief networks
Wilson, K., Quigley, J., Bedford, T., & Walls, L. (2013). Bayes linear graphical models in the design of optimal test strategies. Paper presented at 8th International Conference on Mathematical Methods in Reliability (MMR2013), Stellenboch, South Africa.