Exploring the process of whole system design

This paper explores the adoption of a whole system approach to a more sustainable and innovative design. A case study methodology was utilised to gain improved understanding of whole system design and those factors that substantially influence its success. The paper presents a framework of those factors including the requirement for trans-disciplinary skills, the dynamics of a flattened hierarchy and the need to identify relationships between parts of the system to ultimately optimise the whole. Knowing the factors that influence the process of whole system design provides designers with the knowledge necessary to more effectively work within, manage and facilitate that process. This paper uses anecdotes taken from operational cases, across design contexts, to demonstrate those factors. © 2010 Elsevier Ltd. All rights reserved.

Gaussian Process training with input noise

In standard Gaussian Process regression input locations are assumed to be noise free. We present a simple yet effective GP model for training on input points corrupted by i.i.d. Gaussian noise. To make computations tractable we use a local linear expansion about each input point. This allows the input noise to be recast as output noise proportional to the squared gradient of the GP posterior mean. The input noise variances are inferred from the data as extra hyperparameters. They are trained alongside other hyperparameters by the usual method of maximisation of the marginal likelihood. Training uses an iterative scheme, which alternates between optimising the hyperparameters and calculating the posterior gradient. Analytic predictive moments can then be found for Gaussian distributed test points. We compare our model to others over a range of different regression problems and show that it improves over current methods.

Gaussian Process Vine Copulas for Multivariate Dependence

Copulas allow to learn marginal distributions separately from the multivariate dependence structure (copula) that links them together into a density function. Vine factorizations ease the learning of high-dimensional copulas by constructing a hierarchy of conditional bivariate copulas. However, to simplify inference, it is common to assume that each of these conditional bivariate copulas is independent from its conditioning variables. In this paper, we relax this assumption by discovering the latent functions that specify the shape of a conditional copula given its conditioning variables We learn these functions by following a Bayesian approach based on sparse Gaussian processes with expectation propagation for scalable, approximate inference. Experiments on real-world datasets show that, when modeling all conditional dependencies, we obtain better estimates of the underlying copula of the data.