Neural Network Modelling with Input Uncertainty: Theory and Application

It is generally assumed when using Bayesian inference methods for neural networks that the input data contains no noise. For real-world (errors in variable) problems this is clearly an unsafe assumption. This paper presents a Bayesian neural network framework which accounts for input noise provided that a model of the noise process exists. In the limit where the noise process is small and symmetric it is shown, using the Laplace approximation, that this method adds an extra term to the usual Bayesian error bar which depends on the variance of the input noise process. Further, by treating the true (noiseless) input as a hidden variable, and sampling this jointly with the network’s weights, using a Markov chain Monte Carlo method, it is demonstrated that it is possible to infer the regression over the noiseless input. This leads to the possibility of training an accurate model of a system using less accurate, or more uncertain, data. This is demonstrated on both the, synthetic, noisy sine wave problem and a real problem of inferring the forward model for a satellite radar backscatter system used to predict sea surface wind vectors.

The application of computerised modelling techniques in manufacturing system design

The absence of a definitive approach to the design of manufacturing systems signifies the importance of a control mechanism to ensure the timely application of relevant design techniques. To provide effective control, design development needs to be continually assessed in relation to the required system performance, which can only be achieved analytically through computer simulation. The technique providing the only method of accurately replicating the highly complex and dynamic interrelationships inherent within manufacturing facilities and realistically predicting system behaviour. Owing to the unique capabilities of computer simulation, its application should support and encourage a thorough investigation of all alternative designs. Allowing attention to focus specifically on critical design areas and enabling continuous assessment of system evolution. To achieve this system analysis needs to efficient, in terms of data requirements and both speed and accuracy of evaluation. To provide an effective control mechanism a hierarchical or multi-level modelling procedure has therefore been developed, specifying the appropriate degree of evaluation support necessary at each phase of design. An underlying assumption of the proposal being that evaluation is quick, easy and allows models to expand in line with design developments. However, current approaches to computer simulation are totally inappropriate to support the hierarchical evaluation. Implementation of computer simulation through traditional approaches is typically characterized by a requirement for very specialist expertise, a lengthy model development phase, and a correspondingly high expenditure. Resulting in very little and rather inappropriate use of the technique. Simulation, when used, is generally only applied to check or verify a final design proposal. Rarely is the full potential of computer simulation utilized to aid, support or complement the manufacturing system design procedure. To implement the proposed modelling procedure therefore the concept of a generic simulator was adopted, as such systems require no specialist expertise, instead facilitating quick and easy model creation, execution and modification, through simple data inputs. Previously generic simulators have tended to be too restricted, lacking the necessary flexibility to be generally applicable to manufacturing systems. Development of the ATOMS manufacturing simulator, however, has proven that such systems can be relevant to a wide range of applications, besides verifying the benefits of multi-level modelling.

An investigation into inductive parameter learning in complex hierarchical knowledge structures representing clinical expertise

This dissertation investigates the very important and current problem of modelling human expertise. This is an apparent issue in any computer system emulating human decision making. It is prominent in Clinical Decision Support Systems (CDSS) due to the complexity of the induction process and the vast number of parameters in most cases. Other issues such as human error and missing or incomplete data present further challenges. In this thesis, the Galatean Risk Screening Tool (GRiST) is used as an example of modelling clinical expertise and parameter elicitation. The tool is a mental health clinical record management system with a top layer of decision support capabilities. It is currently being deployed by several NHS mental health trusts across the UK. The aim of the research is to investigate the problem of parameter elicitation by inducing them from real clinical data rather than from the human experts who provided the decision model. The induced parameters provide an insight into both the data relationships and how experts make decisions themselves. The outcomes help further understand human decision making and, in particular, help GRiST provide more accurate emulations of risk judgements. Although the algorithms and methods presented in this dissertation are applied to GRiST, they can be adopted for other human knowledge engineering domains.

Using process-oriented holonic (PrOH) modelling to increase understanding of information systems

Methodologies for understanding business processes and their information systems (IS) are often criticized, either for being too imprecise and philosophical (a criticism often levied at softer methodologies) or too hierarchical and mechanistic (levied at harder methodologies). The process-oriented holonic modelling methodology combines aspects of softer and harder approaches to aid modellers in designing business processes and associated IS. The methodology uses holistic thinking and a construct known as the holon to build process descriptions into a set of models known as a holarchy. This paper describes the methodology through an action research case study based in a large design and manufacturing organization. The scientific contribution is a methodology for analysing business processes in environments that are characterized by high complexity, low volume and high variety where there are minimal repeated learning opportunities, such as large IS development projects. The practical deliverables from the project gave IS and business process improvements for the case study company.

A weakly-supervised Bayesian model for violence detection from social media

Social streams have proven to be the mostup-to-date and inclusive information on cur-rent events. In this paper we propose a novelprobabilistic modelling framework, called violence detection model (VDM), which enables the identification of text containing violent content and extraction of violence-related topics over social media data. The proposed VDM model does not require any labeled corpora for training, instead, it only needs the in-corporation of word prior knowledge which captures whether a word indicates violence or not. We propose a novel approach of deriving word prior knowledge using the relative entropy measurement of words based on the in-tuition that low entropy words are indicative of semantically coherent topics and therefore more informative, while high entropy words indicates words whose usage is more topical diverse and therefore less informative. Our proposed VDM model has been evaluated on the TREC Microblog 2011 dataset to identify topics related to violence. Experimental results show that deriving word priors using our proposed relative entropy method is more effective than the widely-used information gain method. Moreover, VDM gives higher violence classification results and produces more coherent violence-related topics compared toa few competitive baselines.

Alpha helical trans-membrane proteins:enhanced prediction using a Bayesian approach

Membrane proteins, which constitute approximately 20% of most genomes, are poorly tractable targets for experimental structure determination, thus analysis by prediction and modelling makes an important contribution to their on-going study. Membrane proteins form two main classes: alpha helical and beta barrel trans-membrane proteins. By using a method based on Bayesian Networks, which provides a flexible and powerful framework for statistical inference, we addressed alpha-helical topology prediction. This method has accuracies of 77.4% for prokaryotic proteins and 61.4% for eukaryotic proteins. The method described here represents an important advance in the computational determination of membrane protein topology and offers a useful, and complementary, tool for the analysis of membrane proteins for a range of applications.

Hierarchical approach to project planning:the case of a petroleum pipeline construction

Projects exposed to an uncertain environment must be adapted to deal with the effective integration of various planning elements and the optimization of project parameters. Time, cost, and quality are the prime objectives of a project that need to be optimized to fulfill the owner's goal. In an uncertain environment, there exist many other conflicting objectives that may also need to be optimized. These objectives are characterized by varying degrees of conflict. Moreover, an uncertain environment also causes several changes in the project plan throughout its life, demanding that the project plan be totally flexible. Goal programming (GP), a multiple criteria decision making technique, offers a good solution for this project planning problem. There the planning problem is considered from the owner's perspective, which leads to classifying the project up to the activity level. GP is applied separately at each level, and the formulated models are integrated through information flow. The flexibility and adaptability of the models lies in the ease of updating the model parameters at the required level through changing priorities and/or constraints and transmitting the information to other levels. The hierarchical model automatically provides integration among various element of planning. The proposed methodology is applied in this paper to plan a petroleum pipeline construction project, and its effectiveness is demonstrated.