BAYESIAN MODELING OF ANOMALIES DUE TO KNOWN AND UNKNOWN CAUSES

Bayesian modeling of unknown causes of events is an important and pervasive problem. However, it has received relatively little research attention. In general, an intelligent agent (or system) has only limited causal knowledge of the world. Therefore, the agent may well be experiencing the influences of causes outside its model. For example, a clinician may be seeing a patient with a virus that is new to humans; the HIV virus was at one time such an example. It is important that clinicians be able to recognize that a patient is presenting with an unknown disease. In general, intelligent agents (or systems) need to recognize under uncertainty when they are likely to be experiencing influences outside their realm of knowledge. This dissertation investigates Bayesian modeling of unknown causes of events in the context of disease-outbreak detection.The dissertation introduces a Bayesian approach that models and detects (1) known diseases (e.g., influenza and anthrax) by using informative prior probabilities, (2) unknown diseases (e.g., a new, highly contagious respiratory virus that has never been seen before) by using relatively non-informative prior probabilities and (3) partially-known diseases (e.g., a disease that has characteristics of an influenza-like illness) by using semi-informative prior probabilities. I report the results of simulation experiments which support that this modeling method can improve the detection of new disease outbreaks in a population. A key contribution of this dissertation is that it introduces a Bayesian approach for jointly modeling both known and unknown causes of events. Such modeling has broad applicability in artificial intelligence in general and biomedical informatics applications in particular, where the space of known causes of outcomes of interest is seldom complete

Knowledge Discovery with Bayesian Networks

Ph.DDOCTOR OF PHILOSOPH

Intelligent performance assessment in a virtual electronic laboratory

Laboratory work, in the undergraduate engineering course, is aimed at enhancing students’ understanding of taught concepts and integrating theory and practice. This demands that laboratory work is synchronised with lectures in order to maximise its derivable learning outcomes, measurable through assessment. The typical high costs of raditional engineering laboratory, which often militates against its increased use and the synchronisation of laboratory and lectures, have, in addition to other factors, catalysed the increased adoption of virtual laboratories as a complement to the traditional engineering laboratory. In extreme cases, virtual laboratories could serve as alternative means of providing, albeit simulated, meaningful practical experiences. A Virtual Electronic Laboratory (VEL), which can be used to undertake a range of undergraduate electronic engineering curriculum-based laboratory activities, in a realistic manner, has been implemented as part of the work presented in this thesis. The VEL incorporates a Bayesian Network (BN)-based model for the performance assessment of students’ laboratory work in the VEL. Detailed descriptions of the VEL and the assessment model are given. The evaluation of the entire system is in two phases: evaluation of the VEL as a tool for facilitating students’ deeper understanding of fundamental engineering concepts taught in lectures; and evaluation of the assessment model within the context of the VEL environment. The VEL is evaluated at two different engineering faculties, in two separate universities. Results from the evaluation of the VEL show the effectiveness of the VEL to enhance students’ learning, in the light of appropriate learning scenarios, and provide evidence and support for the use of virtual laboratories in the engineering educational context. Performance data, extracted from students’ behaviour logs (captured and recorded during the evaluation of the VEL) are used to evaluate the assessment model. Results of the evaluation demonstrate the effectiveness of the model as an assessment tool, and the practicability of the performance assessment of students’ laboratory work from their observed behaviour in a virtual learning environment.EThOS - Electronic Theses Online ServiceGBUnited Kingdo