State-space inference and learning with Gaussian processes

State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose a new, general methodology for inference and learning in nonlinear state-space models that are described probabilistically by non-parametric GP models. We apply the expectation maximization algorithm to iterate between inference in the latent state-space and learning the parameters of the underlying GP dynamics model. Copyright 2010 by the authors.

Information theory, inference, and learning algorithms

Fun and exciting textbook on the mathematics underpinning the most dynamic areas of modern science and engineering.

Exploring prior experience and the effects of age on product interaction and Learning

The purpose of this research was to investigate the extent to which prior technological experience of products is related to age, and if this has implications for the success of subsequent product interaction. The contribution of this work is to provide the design community with new knowledge and a greater awareness of the diversity of user needs, and particularly the needs and skills of older people. The focus of this paper is to present how individual's mental models of products and interaction were developed through experiential learning; what new knowledge was acquired, and how this contributed to the development of mental models and product understanding. © 2013 Springer-Verlag Berlin Heidelberg.

Bayesian inference and learning in Gaussian process state-space models with Particle MCMC

State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems. We present a fully Bayesian approach to inference and learning (i.e. state estimation and system identification) in nonlinear nonparametric state-space models. We place a Gaussian process prior over the state transition dynamics, resulting in a flexible model able to capture complex dynamical phenomena. To enable efficient inference, we marginalize over the transition dynamics function and, instead, infer directly the joint smoothing distribution using specially tailored Particle Markov Chain Monte Carlo samplers. Once a sample from the smoothing distribution is computed, the state transition predictive distribution can be formulated analytically. Our approach preserves the full nonparametric expressivity of the model and can make use of sparse Gaussian processes to greatly reduce computational complexity.

Ageing, Adaption and Accessibility: Time for the Inclusive Revolution!

Information and Communication Technology (ICT) is becoming increasingly central to many people’s lives, making it possible to be connected in any place at any time, be unceasingly and instantly informed, and benefit from greater economic and educational opportunities. With all the benefits afforded by these new-found capabilities, however, come potential drawbacks. A plethora of new PCs, laptops, tablets, smartphones, Bluetooth, the internet, Wi-Fi (the list goes on) expect us to know or be able to guess, what, where and when to connect, click, double-click, tap, flick, scroll, in order to realise these benefits, and to have the physical and cognitive capability to do all these things. One of the groups most affected by this increase in high-demand technology is older people. They do not understand and use technology in the same way that younger generations do, because they grew up in the simpler electro-mechanical era and embedded that particular model of the world in their minds. Any consequential difficulty in familiarising themselves with modern ICT and effectively applying it to their needs can also be exacerbated by age-related changes in vision, motor control and cognitive functioning. Such challenges lead to digital exclusion. Much has been written about this topic over the years, usually by academics from the area of inclusive product design. The issue is complex and it is fair to say that no one researcher has the whole picture. It is difficult to understand and adequately address the issue of digital exclusion among the older generation without looking across disciplines and at industry’s and government’s understanding, motivation and efforts toward resolving this important problem. To do otherwise is to risk misunderstanding the true impact that ICT has and could have on people’s lives across all generations. In this European year of Active Ageing and Solidarity between Generations and as the British government is moving forward with its Digital by Default initiative as part of a wider objective to make ICT accessible to as many people as possible by 2015, the Engineering Design Centre (EDC) at the University of Cambridge collaborated with BT to produce a book of thought pieces to address, and where appropriate redress, these important and long-standing issues. “Ageing, Adaption and Accessibility: Time for the Inclusive Revolution!” brings together opinions and insights from twenty one prominent thought leaders from government, industry and academia regarding the problems, opportunities and strategies for combating digital exclusion among senior citizens. The contributing experts were selected as individuals, rather than representatives of organisations, to provide the broadest possible range of perspectives. They are renowned in their respective fields and their opinions are formed not only from their own work, but also from the contributions of others in their area. Their views were elicited through conversations conducted by the editors of this book who then drafted the thought pieces to be edited and approved by the experts. We hope that this unique collection of thought pieces will give you a broader perspective on ageing, people’s adaption to the ever changing world of technology and insights into better ways of designing digital devices and services for the older population.

Statistics anxiety, trait anxiety, learning behavior, and academic performance

The present study investigated the relationship between statistics anxiety, individual characteristics (e.g., trait anxiety and learning strategies), and academic performance. Students enrolled in a statistics course in psychology (N=147) filled in a questionnaire on statistics anxiety, trait anxiety, interest in statistics, mathematical selfconcept, learning strategies, and procrastination. Additionally, their performance in the examination was recorded. The structural equation model showed that statistics anxiety held a crucial role as the strongest direct predictor of performance. Students with higher statistics anxiety achieved less in the examination and showed higher procrastination scores. Statistics anxiety was related indirectly to spending less effort and time on learning. Trait anxiety was related positively to statistics anxiety and, counterintuitively, to academic performance. This result can be explained by the heterogeneity of the measure of trait anxiety. The part of trait anxiety that is unrelated to the specific part of statistics anxiety correlated positively with performance.