Towards Qualitative Approaches to Bayesian Evidential Reasoning

A crucial aspect of evidential reasoning in crime investigation involves comparing the support that evidence provides for alternative hypotheses. Recent work in forensic statistics has shown how Bayesian Networks (BNs) can be employed for this purpose. However, the specification of BNs requires conditional probability tables describing the uncertain processes under evaluation. When these processes are poorly understood, it is necessary to rely on subjective probabilities provided by experts. Accurate probabilities of this type are normally hard to acquire from experts. Recent work in qualitative reasoning has developed methods to perform probabilistic reasoning using coarser representations. However, the latter types of approaches are too imprecise to compare the likelihood of alternative hypotheses. This paper examines this shortcoming of the qualitative approaches when applied to the aforementioned problem, and identifies and integrates techniques to refine them.

Mature workers, training and using TLM frameworks

Mature workers have been at the centre of policies aimed at encouraging higher workforce participation, longer working life and enhanced savings for retirement. Low mature age workforce participation rates reflect labour market withdrawal in the face of multiple barriers to participation for many. Their apparent voluntary joblessness conceals the fact that mature workers endure longer periods of unemployment, discrimination, redundancy and other barriers to employment (hence the drift to 'early retirement'). The policy dilemma is not just about addressing discrimination barriers, access to appropriate retraining or skills enhancement for mature workers, but what this tells us about lifelong learning as a means of managing and mitigating risk. The mismatch between work opportunities/skills shortages and the low education and skills base of many mature workers, means it is simplistic to think that working longer might be a short term way to address skills shortages; without an enormous investment in the current ageing cohort. Drawing on Transitional Labour Market (TLM) theory and European reform agendas, this article argues that the link between investment in lifelong education/ skills training and stronger labour market participation needs attention; not just for current cohorts of excluded or underemployed mature workers but to position strategically for future generations.

Evolutionary structure learning algorithm for Bayesian network and penalized mutual information metric

This paper formulates the problem of learning Bayesian network structures from data as determining the structure that best approximates the probability distribution indicated by the data. A new metric, Penalized Mutual Information metric, is proposed, and a evolutionary algorithm is designed to search for the best structure among alternatives. The experimental results show that this approach is reliable and promising.

An objective function based on Bayesian likelihoods of necessity and sufficiency for concept learning in the absence of labeled counter-examples

Supervised machine learning techniques generally require that the training set on which learning is based contain sufficient examples representative of the target concept, as well as known counter-examples of the concept; however, in many application domains it is not possible to supply a set of labeled counter-examples. This paper proposes an objective function based on Bayesian likelihoods of necessity and sufficiency. This function can be used to guide search towards the discovery of a concept description given only a set of labeled positive examples of the target concept, and as a corpus of unlabeled examples. Results of experiments performed on several datasets from the VCI repository show that the technique achieves comparable accuracy to conventional supervised learning techniques, despite the fact that the latter require a set of labeled counter-examples to be supplied. The technique can be applied in many domains in which the provision of labeled counter-examples is problematic.

Comparison of MLP and Bayesian approaches on mineral prospectivity mapping tasks

Mineral Prospectivity Mapping is the process of combining maps containing different geoscientific data sets to produce a single map depicting areas ranked according to their potential to host mineral deposits of a particular type. This paper outlines two approaches for deriving a function which can be used to assign to each cell in the study area a value representing the posterior probability that the cell contains a deposit of the sought-after mineral. One approach is based on estimating probability density functions (pdfs); the second uses multilayer perceptrons (MLPs). Results are provided from applying these approaches to geoscientific datasets covering a region in North Western Victoria, Australia. The results demonstrate that while both the Bayesian approach and the MLP approach yield similar results when the number of input dimensions is small, the Bayesian approach rapidly becomes unstable as the number of input dimensions increases, with the resulting maps displaying high sensitivity to the number of mixtures used to model the distributions. However, despite the fact that Bayesian assigned values cannot be interpreted as posterior probabilities in high dimensional input spaces, the pixel favorability rankings produced by the two methods is similar.

Transforming frameworks: new approaches to CCTs and the pursuit of educational equity

Teacher educators throughout the world are increasingly under pressure to develop educational programs and school-based relationships which respond simultaneously to the multiple consequences of changed and changing technologies, new understandings of identity (what it means to be a teacher and a student) and persistently uneven pattems of educational (and social) success. Responses to these challenges regularly draw upon computer and communication technologies (CCTs) in the sometimes optimistic belief that this will improve the chance of any educational reform having a positive impact on students at risk of educational alienation and failure. Unfortunately, the gap between the hopeful embrace of technology and the actual outcomes delivered by technologically mediated educational innovations is often quite considerable. This paper investigates the kinds of educational conversations that are necessary to allow us to move beyond these optimistic adoptions of technology to address long standing patterns of educational success and failure and outlines a framework for transformative work in this area.