Developments Under the Freedom of Information Act

This article reviews authors' recently developed algorithm for identification of nonlinear state-space models under missing observations and extends it to the case of unknown model structure. In order to estimate the parameters in a state-space model, one needs to know the model structure and have an estimate of states. If the model structure is unknown, an approximation of it is obtained using radial basis functions centered around a maximum a posteriori estimate of the state trajectory. A particle filter approximation of smoothed states is then used in conjunction with expectation maximization algorithm for estimating the parameters. The proposed approach is illustrated through a real application

On the construction of probabilistic Newton-type algorithms

It has recently been shown that many of the existing quasi-Newton algorithms can be formulated as learning algorithms, capable of learning local models of the cost functions. Importantly, this understanding allows us to safely start assembling probabilistic Newton-type algorithms, applicable in situations where we only have access to noisy observations of the cost function and its derivatives. This is where our interest lies. We make contributions to the use of the non-parametric and probabilistic Gaussian process models in solving these stochastic optimisation problems. Specifically, we present a new algorithm that unites these approximations together with recent probabilistic line search routines to deliver a probabilistic quasi-Newton approach. We also show that the probabilistic optimisation algorithms deliver promising results on challenging nonlinear system identification problems where the very nature of the problem is such that we can only access the cost function and its derivative via noisy observations, since there are no closed-form expressions available

The future of non-terrestrial broadcasters in the UK television industry: the shape of things to come?

The UK television industry operates in a highly turbulent environment characterised by the rapid changes in; regulation, technology, audience behaviour and new media consumption. This competitive environment makes it difficult for television companies to identify strategies for growth and may indeed place their very survival at risk. This paper investigates the future of the non-terrestrial television broadcasters (NTBs) in the UK and examines how they can maintain and develop their position as major outlets for television consumption over the next five years. Empirical data was collected using a scenario planning methodology which is widely known for its value in addressing environmental uncertainty by illustrating the future as having a limited number of possible. A range of experienced industry practitioners participated in the development of four future scenarios, based on the degree of technological convergence and the number of television gatekeepers present in the industry. Having evaluated the implications of each scenario, and identified a number of early indicators that would signpost which was most likely to come about, the participants suggested three strategic options that non-terrestrial television broadcasters might adopt to compete effectively. These strategies included; investing in and owning original and exclusive content; forming strategic partnerships with other media companies; making significant investment in channel brands. Key Words: UK Television Industry, Non-Terrestrial Broadcasters, Scenario Planning, Competitive Strategy

Newton-based maximum likelihood estimation in nonlinear state space models

Maximum likelihood (ML) estimation using Newton's method in nonlinear state space models (SSMs) is a challenging problem due to the analytical intractability of the log-likelihood and its gradient and Hessian. We estimate the gradient and Hessian using Fisher's identity in combination with a smoothing algorithm. We explore two approximations of the log-likelihood and of the solution of the smoothing problem. The first is a linearization approximation which is computationally cheap, but the accuracy typically varies between models. The second is a sampling approximation which is asymptotically valid for any SSM but is more computationally costly. We demonstrate our approach for ML parameter estimation on simulated data from two different SSMs with encouraging results.Comment: 17 pages, 2 figures. Accepted for the 17th IFAC Symposium on System Identification (SYSID), Beijing, China, October 201

A Bayesian Filtering Algorithm for Gaussian Mixture Models

A Bayesian filtering algorithm is developed for a class of state-space systems that can be modelled via Gaussian mixtures. In general, the exact solution to this filtering problem involves an exponential growth in the number of mixture terms and this is handled here by utilising a Gaussian mixture reduction step after both the time and measurement updates. In addition, a square-root implementation of the unified algorithm is presented and this algorithm is profiled on several simulated systems. This includes the state estimation for two non-linear systems that are strictly outside the class considered in this paper