informs ® doi 10.1287/mnsc.1050.0379

We use the lower and upper predictive probabilities from Coolen [5] to compare future numbers of successes in Bernoulli trials for different groups. We consider both pairwise and multiple comparisons. These inferences are in terms of lower and upper probabilities that the number of successes in m future trials from one group exceeds the number of successes in m future trials from another group, or such numbers from all other groups. We analyse these lower and upper probabilities via application to two data sets from the literature, and discuss the imprecision in relation to m.

Stochastic inverse problems in heat conduction with consideration of uncertainties in measured temperature data, thermal sensor location and material thermal properties are addressed using a Bayesian statistical inference method. Both parameter estimation and thermal history reconstruction problems, including boundary heat flux and heat source reconstruction, are studied. The probabilistic specifications of these unknown variables are deduced from temperature measurements. A joint probability distribution approach is taken to specify the conditional (on data) state space of random unknown quantities by multiplying the likelihood and prior distribution functions. Hierarchical Bayesian models are adopted to relax the prior assumptions on the unknowns. The use of a hierarchical Bayesian method for automatic selection of regularization parameter for function estimation inverse problems is discussed. This methodology explores the multi-scale spatial prior models in estimation of temporalspatially varying thermal quantities. Due to the high dimensionality and implicit form of the posterior distribution, Markov chain Monte Carlo (MCMC) simulation is conducted to explore the posterior state space. The methodologies presented are general and applicable to a number of data-driven engineering inverse problems.

Ecological regions are increasingly used as a spatial unit for planning and environmental management. It is important to define these regions in a scientifically defensible way to justify any decisions made on the basis that they are representative of broad environmental assets. The paper describes a methodology and tool to identify cohesive bioregions. The methodology applies an elicitation process to obtain geographical descriptions for bioregions, each of these is transformed into a Normal density estimate on environmental variables within that region. This prior information is balanced with data classification of environmental datasets using a Bayesian statistical modelling approach to objectively map ecological regions. The method is called model-based clustering as it fits a Normal mixture model to the clusters associated with regions, and it addresses issues of uncertainty in environmental datasets due to overlapping clusters.

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leading to a major in Information Science within the BCom, BA and BSc degrees. In addition to undergraduate teaching, the department is also strongly involved in postgraduate research programmes leading to MCom, MA, MSc and PhD degrees. Research projects in spatial information processing, connectionist-based information systems, software engineering and software development, information engineering and database, software metrics, distributed information systems, multimedia information systems and information systems security are particularly well supported. The views expressed in this paper are not necessarily those of the department as a whole. The accuracy of the information presented in this paper is the sole responsibility of the authors. Copyright Copyright remains with the authors. Permission to copy for research or teaching purposes is granted on the condition that the authors and the Series are given due acknowledgment. Reproduction in any form for purposes other than research or teaching is forbidden unless prior written permission has been obtained from the authors. Correspondence This paper represents work to date and may not necessarily form the basis for the authors’ final conclusions relating to this topic. It is likely, however, that the paper will appear in some form in a journal or in conference proceedings in the near future. The authors would be pleased to receive correspondence in connection with any of the issues raised in this paper, or for subsequent publication details. Please write directly to the authors at the address provided below. (Details of final journal/conference publication venues for these papers are also provided on the Department’s publications web pages:

Abstract. Prediction of mechanical, thermal, and chemical actions induced during man-made accidents (accidental actions) is of crucial importance to assessing potential damage to structures exposed to these actions. A logical result of such a prediction may be expressed in the form of probabilistic models describing likelihood of occurrence and characteristics of accidental actions. For many types of accidental actions the models are to be selected under the conditions of incomplete knowledge about and/or scarce statistical information on intensities and likelihood of imposition of the actions. This paper proposes a simulation-based procedure intended for a selection of the probabilistic models under these conditions. The proposed procedure is formulated in the context of the classical Bayesian approach to risk assessment. The main idea of it is that statistical samples necessary for fitting the probabilistic action models can be acquired from a stochastic simulation of accident sequences leading to an imposition of accidental actions. Formally, the stochastic simulation of accidents serves the purpose of propagating uncertainties related to the physical phenomena capable of inducing accidental actions. These uncertainties are quantified in line with the classical Bayesian approach. The simulation-based procedure can be used for damage assessment and risk studies within the methodological framework provided by the above-mentioned approach.

Large quantities of measured data are being routinely collected in a variety of industries and used for extracting linear models for tasks such as, process control, fault diagnosis and process monitoring. However, existing linear modeling methods do not fully utilize all the information contained in the measurements. This paper presents a new approach for linear process modeling that makes maximum use of available process data and process knowledge. This approach, called Bayesian Latent Variable Regression (BLVR), permits extraction and incorporation of knowledge about the statistical behavior of measurements in developing linear process models. Furthermore, unlike existing methods, BLVR is able to handle noise in inputs and outputs, collinear variables, and incorporate prior knowledge about the regression parameters and measured variables. The resulting model is usually more accurate than that obtained by existing methods including, OLS, PCR and PLS. In this paper, BLVR considers a univariate output, and assumes the underlying variables and noise to be Gaussian, but the approach may be easily used for multivariate outputs and other distributions. An empirical Bayes approach is developed to extract the prior information from historical data or from the maximum likelihood solution of available data. Illustrative examples of steady state, dynamic and inferential modeling demonstrate the superior accuracy of BLVR over existing methods even when the assumptions of Gaussian distributions are violated. The relationship between BLVR and existing methods and opportunities for future work based on the proposed framework are also discussed. 1.

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In this paper we present both theoretical structure and empirical validation of an on-line questionnaire to measure learners self-rated motivation, learning strategies, learning styles and social abilities. Profiling information of the measurement instrument has been utilised in various learning management systems in Finnish distance learning courses. The Abilities for Computer Assisted Learning Questionnaire I (ACALQ) contains 48 items measuring following dimensions: (1) motivation, 12 items; (2) learning strategies, 10 items; (3) learning styles, 6 items; (4) social abilities, 12 items; (5) serialistic-holistic approach (8 items); and (6) signaling (8 items). The theoretical structure of the first four parts of the instrument was analysed with the following three empirical samples (n=328): Finnish elementary school 5th and 6th grade children (age median 11 years, n=166), Finnish university students (age median 21 years, n=112) and Finnish post graduate adult learners preparing their dissertations (age median 27 years, n=50). The results of Bayesian network modeling show that the structure of the ACALQ is valid for all the three groups except for the third dimension (“Learning styles”) that was not found in the adolescent and adult samples. Results of CFA show that all the optimised solutions (except the 26-item solution for the sample 1) surpass the baseline model by both comparative fit index and Tucker-Lewis coefficient.