Uncertainty explicit assessment of off-the-shelf software: A Bayesian approach

Assessment of software COTS components is an essential part of component-based software development. Poorly chosen components may lead to solutions of low quality and that are difficult to maintain. The assessment may be based on incomplete knowledge about the COTS component itself and other aspects (e.g. vendor’s credentials, etc.), which may affect the decision of selecting COTS component(s). We argue in favor of assessment methods in which uncertainty is explicitly represented (‘uncertainty explicit’ methods) using probability distributions. We provide details of a Bayesian model, which can be used to capture the uncertainties in the simultaneous assessment of two attributes, thus, also capturing the dependencies that might exist between them. We also provide empirical data from the use of this method for the assessment of off-the-shelf database servers which illustrate the advantages of ‘uncertainty explicit’ methods over conventional methods of COTS component assessment which assume that at the end of the assessment the values of the attributes become known with certainty

Defining and characterising structural uncertainty in decision analytic models

An inappropriate structure for a decision analytic model can potentially invalidate estimates of cost-effectiveness and estimates of the value of further research. However, there are often a number of alternative and credible structural assumptions which can be made. Although it is common practice to acknowledge potential limitations in model structure, there is a lack of clarity about methods to characterize the uncertainty surrounding alternative structural assumptions and their contribution to decision uncertainty. A review of decision models commissioned by the NHS Health Technology Programme was undertaken to identify the types of model uncertainties described in the literature. A second review was undertaken to identify approaches to characterise these uncertainties. The assessment of structural uncertainty has received little attention in the health economics literature. A common method to characterise structural uncertainty is to compute results for each alternative model specification, and to present alternative results as scenario analyses. It is then left to decision maker to assess the credibility of the alternative structures in interpreting the range of results. The review of methods to explicitly characterise structural uncertainty identified two methods: 1) model averaging, where alternative models, with different specifications, are built, and their results averaged, using explicit prior distributions often based on expert opinion and 2) Model selection on the basis of prediction performance or goodness of fit. For a number of reasons these methods are neither appropriate nor desirable methods to characterize structural uncertainty in decision analytic models. When faced with a choice between multiple models, another method can be employed which allows structural uncertainty to be explicitly considered and does not ignore potentially relevant model structures. Uncertainty can be directly characterised (or parameterised) in the model itself. This method is analogous to model averaging on individual or sets of model inputs, but also allows the value of information associated with structural uncertainties to be resolved.

Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version

Psychometrics in Practice at RCEC

A broad range of topics is dealt with in this volume: from combining the psychometric generalizability and item response theories to the ideas for an integrated formative use of data-driven decision making, assessment for learning and diagnostic testing. A number of chapters pay attention to computerized (adaptive) and classification testing. Other chapters treat the quality of testing in a general sense, but for topics like maintaining standards or the testing of writing ability, the quality of testing is dealt with more specifically.\ud All authors are connected to RCEC as researchers. They present one of their current research topics and provide some insight into the focus of RCEC. The selection of the topics and the editing intends that the book should be of special interest to educational researchers, psychometricians and practitioners in educational assessment