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.

Performance of magnesia cements in porous blocks in acid and magnesium environments

The performance of porous blocks containing three different reactive magnesia-based cements - namely magnesia alone, magnesium oxide: Portland cement (PC) in 1:1 ratio, cured in ambient conditions, and magnesia alone, cured at elevated carbon dioxide conditions, in hydrochloric acid and magnesium sulfate solution - was investigated. Different aggressive chemical solution conditions were used, to which the samples were exposed for up to 12 months and then tested for strength and microstructure. The performance was also compared with that of standard PC-based blocks. The results showed the significant resistance to chemical attack offered by magnesia, both alone and with PC blend in the porous blocks when cured under ambient carbon dioxide conditions, and confirmed the much poorer performance of blocks made from PC alone. The blocks of solely magnesia cured in elevated carbon dioxide conditions, at 20% concentration, showed slightly lower resistance to acid attack than PC; however, the resistance to sulfate attack was much higher. © 2012 Thomas Telford Ltd.

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.