Flexible Gaussian process wind field models

This technical report builds on previous reports to derive the likelihood and its derivatives for a Gaussian Process with a modified Bessel function based covariance function. The full derivation is shown. The likelihood (with gradient information) can be used in maximum likelihood procedures (i.e. gradient based optimisation) and in Hybrid Monte Carlo sampling (i.e. within a Bayesian framework).

A flexible, extensible object oriented real-time near photorealistic visualization system:the system framework design

In this paper we describe a novel, extensible visualization system currently under development at Aston University. We introduce modern programming methods, such as the use of data driven programming, design patterns, and the careful definition of interfaces to allow easy extension using plug-ins, to 3D landscape visualization software. We combine this with modern developments in computer graphics, such as vertex and fragment shaders, to create an extremely flexible, extensible real-time near photorealistic visualization system. In this paper we show the design of the system and the main sub-components. We stress the role of modern programming practices and illustrate the benefits these bring to 3D visualization. © 2006 Springer-Verlag Berlin Heidelberg.

e-CAF: flexible marking schemes for electronic coursework assessment and feedback

Recent surveys reveal that many university students in the U.K. are not satisfied with the timeliness and usefulness of the feedback given by their tutors. Ensuring timeliness in marking can result in a reduction in the quality of feedback. Though suitable use of Information and Communication Technology should alleviate this problem, existing Virtual Learning Environments are inadequate to support detailed marking scheme creation and they provide little support for giving detailed feedback. This paper describes a unique new web-based tool called e-CAF for facilitating coursework assessment and feedback management directed by marking schemes. Using e-CAF, tutors can create or reuse detailed marking schemes efficiently without sacrificing the accuracy or thoroughness in marking. The flexibility in marking scheme design also makes it possible for tutors to modify a marking scheme during the marking process without having to reassess the students’ submissions. The resulting marking process will become more transparent to students.

Projected sequential Gaussian processes: flexible interpolation for large data sets

Recently within the machine learning and spatial statistics communities many papers have explored the potential of reduced rank representations of the covariance matrix, often referred to as projected or fixed rank approaches. In such methods the covariance function of the posterior process is represented by a reduced rank approximation which is chosen such that there is minimal information loss. In this paper a sequential framework for inference in such projected processes is presented, where the observations are considered one at a time. We introduce a C++ library for carrying out such projected, sequential estimation which adds several novel features. In particular we have incorporated the ability to use a generic observation operator, or sensor model, to permit data fusion. We can also cope with a range of observation error characteristics, including non-Gaussian observation errors. Inference for the variogram parameters is based on maximum likelihood estimation. We illustrate the projected sequential method in application to synthetic and real data sets. We discuss the software implementation and suggest possible future extensions.