Novelty, or abnormality, detection aims to identify patterns within data streams that do not conform to expected behaviour. This paper introduces a novelty detection technique using a combination of Gaussian Processes and extreme value theory to identify anomalous behaviour in streaming data. The proposed combination of continuous and count stochastic processes is a principled approach towards dynamic extreme value modeling that accounts for the dynamics in the time series, the streaming nature of its observation as well as its sampling process. The approach is tested on both synthetic and real data, showing itself to be effective in our primary application of maritime vessel track analysis.

In this paper, we elucidate the equivalence between inference in game theory and machine learning. Our aim in so doing is to establish an equivalent vocabulary between the two domains so as to facilitate developments at the intersection of both fields, and as proof of the usefulness of this approach, we use recent developments in each field to make useful improvements to the other. More specifically, we consider the analogies between smooth best responses in fictitious play and Bayesian inference methods. Initially, we use these insights to develop and demonstrate an improved algorithm for learning in games based on probabilistic moderation. That is, by integrating over the distribution of opponent strategies (a Bayesian approach within machine learning) rather than taking a simple empirical average (the approach used in standard fictitious play) we derive a novel moderated fictitious play algorithm and show that it is more likely than standard fictitious play to converge to a payoff-dominant but risk-dominated Nash equilibrium in a simple coordination game. Furthermore we consider the converse case, and show how insights from game theory can be used to derive two improved mean field variational learning algorithms. We first show that the standard update rule of mean field variational learning is analogous to a Cournot adjustment within game theory. By analogy with fictitious play, we then suggest an improved update rule, and show that this results in fictitious variational play, an improved mean field variational learning algorithm that exhibits better convergence in highly or strongly connected graphical models. Second, we use a recent advance in fictitious play, namely dynamic fictitious play, to derive a derivative action variational learning algorithm, that exhibits superior convergence properties on a canonical machine learning problem (clustering a mixture distribution).

As service-oriented environments grow in size and complexity, managing their performance becomes increasingly difficult. To assist administrators, autonomic techniques have been adopted to permit these environments to be self-managing (problem localization, workload management, etc.). These techniques need a sense of system state and the ability to project a new state given some change within the environment. Recent work addressing this issue frequently used statistically learned models which were derived entirely from data. However, many environments already have management facilities in place that could provide precise and useful insights (e.g. workflows) into the system. This paper introduces a method of modeling service-oriented system performance using Bayesian networks and specifically addresses the benefits obtained by incorporating these insights into the model learning process. To further minimize model building costs, we devise a decentralized method to concurrently learn parts of the model where knowledge inclusion is impossible. Simulations and applications in actual environments show significant reductions in learning time, better accuracy and stronger tolerance to small learning data sets.

The application of anaesthetic agents is known to have significant effects on the EEG waveform. Information extraction now routinely goes beyond second order spectral analysis, as obtained via power spectral methods, and uses higher order spectral methods. In this paper we present a model which generalises the autoregressive class of polyspectral models by having a semi-parametric description of the residual probability density. We estimate the model in the variational Bayesian framework and extract higher order spectral features. Testing their importance for depth of anaesthesia classification is done on three different EEG data sets collected under exposure to different agents. The results show that significant improvements can be made over standard methods of estimating higher order spectra. The results also indicate that in two out of three anaesthetic agents, better classification can be achieved with higher order spectral features.

We report about an automatic continuous sleep stager which is based on probabilistic principles employing hidden Markov models (HMMs). Our sleep stager offers the advantage of being objective by not relying on human scorers, having much finer temporal resolution (1 second instead of 30 second): and being based on solid probabilistic principles rather than a predefined set of rules. Results obtained for nine whole night sleep recordings are reported.

A Bayesian-based methodology is presented which automatically penalizes overcomplex models being fitted to unknown data. We show that, with a Gaussian mixture model, the approach is able to select an "optimal" number of components in the model and so partition data sets. The performance of the Bayesian method is compared to other methods of optimal model selection and found to give good results. The methods are tested on synthetic and real data sets.