To secure today's computer systems, it is critical to have different intrusion detection sensors embedded in them. The complexity of distributed computer systems makes it difficult to determine the appropriate choice and placement of these detectors because there are many possib…
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System health monitoring and sensor placement are areas of great technical and scientific interest. Prognostics and health management of a complex system require multiple sensors to extract required information from the sensed environment, because no single sensor can obtain all…
Probabilistic inference using Bayesian networks is now a well-established approach for reasoning under uncertainty. Among many efficiency-driven techniques which have been developed, the Optimal Factoring Problem (OFP) is distinguished for presenting a combinatorial optimization…
This thesis investigates the use of Bayesian Networks (BNs), augmented by the Dynamic Discretization Algorithm, to model a variety of clinical problems. In particular, the thesis demonstrates four novel applications of BN and dynamic discretization to clinical problems. Firstly,…
Construction disputes are recognised as unpleasant for diverting valuable and limited project resources from the overall aim of project management. A model predicting the occurrence of construction disputes is desirable in this case. Prediction dictates sufficient understanding …
One of the hardest challenges in building a realistic Bayesian network (BN) model is to construct the node probability tables (NPTs). Even with a fixed predefined model structure and very large amounts of relevant data, machine learning methods do not consistently achieve great …
Evidence based medicine (EBM) is defined as the use of best available evidence for decision making, and it has been the predominant paradigm in clinical decision making for the last 20 years. EBM requires evidence from multiple sources to be combined, as published results may no…
This dissertation defends the so-called 'agency-approach' to causation, which attempts to ground the causal relation in the cause's role of being a means to bring about its effect. The defence is confined to a conceptual interpretation of this theory, pertaining to the concept o…
The Bayesian network (BN) formalism is a powerful representation for encoding domains characterised by uncertainty. However, before it can be used it must first be constructed, which is a major challenge for any real-life problem. There are two broad approaches, namely the hand-…