6 research outputs found
Modeling qualitative judgements in Bayesian networks
PhDAlthough Bayesian Networks (BNs) are increasingly being used to solve real world
problems [47], their use is still constrained by the difficulty of constructing the node
probability tables (NPTs). A key challenge is to construct relevant NPTs using the
minimal amount of expert elicitation, recognising that it is rarely cost-effective to elicit
complete sets of probability values.
This thesis describes an approach to defining NPTs for a large class of commonly
occurring nodes called ranked nodes. This approach is based on the doubly truncated
Normal distribution with a central tendency that is invariably a type of a weighted
function of the parent nodes.
We demonstrate through two examples how to build large probability tables using
the ranked nodes approach. Using this approach we are able to build the large probability
tables needed to capture the complex models coming from assessing firm's risks in the
safety or finance sector.
The aim of the first example with the National Air-Traffic Services(NATS) is to
show that using this approach we can model the impact of the organisational factors
in avoiding mid-air aircraft collisions. The resulting model was validated by NATS and
helped managers to assess the efficiency of the company handling risks and thus, control
the likelihood of air-traffic incidents. In the second example, we use BN models to capture
the operational risk (OpRisk) in financial institutions. The novelty of this approach is
the use of causal reasoning as a means to reduce the uncertainty surrounding this type of
risk. This model was validated against the Basel framework [160], which is the emerging
international standard regulation governing how financial institutions assess OpRisks.EPSRC funded SCORE project (Sensing
Changes in Operational Risk Exposure)