A basis function approach to Bayesian inference in diffusion processes

In this paper, we present a framework for Bayesian inference in continuous-time diffusion processes. The new method is directly related to the recently proposed variational Gaussian Process approximation (VGPA) approach to Bayesian smoothing of partially observed diffusions. By adopting a basis function expansion (BF-VGPA), both the time-dependent control parameters of the approximate GP process and its moment equations are projected onto a lower-dimensional subspace. This allows us both to reduce the computational complexity and to eliminate the time discretisation used in the previous algorithm. The new algorithm is tested on an Ornstein-Uhlenbeck process. Our preliminary results show that BF-VGPA algorithm provides a reasonably accurate state estimation using a small number of basis functions.

Variational inference for diffusion processes

Diffusion processes are a family of continuous-time continuous-state stochastic processes that are in general only partially observed. The joint estimation of the forcing parameters and the system noise (volatility) in these dynamical systems is a crucial, but non-trivial task, especially when the system is nonlinear and multimodal. We propose a variational treatment of diffusion processes, which allows us to compute type II maximum likelihood estimates of the parameters by simple gradient techniques and which is computationally less demanding than most MCMC approaches. We also show how a cheap estimate of the posterior over the parameters can be constructed based on the variational free energy.

Derivations of variational gaussian process approximation framework

Recently, within the VISDEM project (EPSRC funded EP/C005848/1), a novel variational approximation framework has been developed for inference in partially observed, continuous space-time, diffusion processes. In this technical report all the derivations of the variational framework, from the initial work, are provided in detail to help the reader better understand the framework and its assumptions.

Variational Markov Chain Monte Carlo for Bayesian smoothing of non-linear niffusions

In this paper we develop set of novel Markov chain Monte Carlo algorithms for Bayesian smoothing of partially observed non-linear diffusion processes. The sampling algorithms developed herein use a deterministic approximation to the posterior distribution over paths as the proposal distribution for a mixture of an independence and a random walk sampler. The approximating distribution is sampled by simulating an optimized time-dependent linear diffusion process derived from the recently developed variational Gaussian process approximation method. Flexible blocking strategies are introduced to further improve mixing, and thus the efficiency, of the sampling algorithms. The algorithms are tested on two diffusion processes: one with double-well potential drift and another with SINE drift. The new algorithm's accuracy and efficiency is compared with state-of-the-art hybrid Monte Carlo based path sampling. It is shown that in practical, finite sample, applications the algorithm is accurate except in the presence of large observation errors and low observation densities, which lead to a multi-modal structure in the posterior distribution over paths. More importantly, the variational approximation assisted sampling algorithm outperforms hybrid Monte Carlo in terms of computational efficiency, except when the diffusion process is densely observed with small errors in which case both algorithms are equally efficient.

Ionic and molecular diffusion in cementitious materials

The work described in this thesis is an attempt to provide improved understanding of the effects of several factors affecting diffusion in hydrated cement pastes and to aid the prediction of ionic diffusion processes in cement-based materials. Effect of pore structure on diffusion was examined by means of comparative diffusion studies of quaternary ammonium ions with different ionic radii. Diffusivities of these ions in hydrated pastes of ordinary portland cement with or without addition of fly ash were determined by a quasi-steady state technique. The restriction of the pore geometry on diffusion was evaluated from the change of diffusivity in response to the change of ionic radius. The pastes were prepared at three water-cement ratios, 0.35, 0.50 and 0.65. Attempts were made to study the effect of surface charge or the electrochemical double layer at the pore/solution interface on ionic diffusion. An approach was to evaluate the zeta potentials of hydrated cement pastes through streaming potential measurements. Another approach was the comparative studies of the diffusion kinetics of chloride and dissolved oxygen in hydrated pastes of ordinary portland cement with addition of 0 and 20% fly ash. An electrochemical technique for the determination of oxygen diffusivity was also developed. Non-steady state diffusion of sodium potassium, chloride and hydroxyl ions in hydrated ordinary portland cement paste of water-cement ratio 0.5 was studied with the aid of computer-modelling. The kinetics of both diffusion and ionic binding were considered for the characterization of the concentration profiles by Fick's first and second laws. The effect of the electrostatic interactions between ions on the overall diffusion rates was also considered. A general model concerning the prediction of ionic diffusion processes in cement-based materials has been proposed.

Opinion leaders' role in innovation diffusion:a simulation study

This study investigates the critical role that opinion leaders (or influentials) play in the adoption process of new products. Recent existing reseach evidence indicates a limited effect of opinion leaders on diffusion processes, yet these studies take into account merely the network position of opinion leaders without addressing their influential power. Empirical findings of our study show that opinion leaders, in addition to having a more central network position, possess more accurate knowledge about a product and tend to be less susceptible to norms and more innovative. Experiments that address these attributes, using an agent-based model, demonstrate that opinion leaders increase the speed of the information stream and the adoption process itself. Furthermore, they increase the maximum adoption percentage. These results indicate that targeting opinion leaders remains a valuable marketing strategy.

Variational Markov chain Monte Carlo for Bayesian smoothing of non-linear diffusions

In this paper we develop set of novel Markov Chain Monte Carlo algorithms for Bayesian smoothing of partially observed non-linear diffusion processes. The sampling algorithms developed herein use a deterministic approximation to the posterior distribution over paths as the proposal distribution for a mixture of an independence and a random walk sampler. The approximating distribution is sampled by simulating an optimized time-dependent linear diffusion process derived from the recently developed variational Gaussian process approximation method. The novel diffusion bridge proposal derived from the variational approximation allows the use of a flexible blocking strategy that further improves mixing, and thus the efficiency, of the sampling algorithms. The algorithms are tested on two diffusion processes: one with double-well potential drift and another with SINE drift. The new algorithm's accuracy and efficiency is compared with state-of-the-art hybrid Monte Carlo based path sampling. It is shown that in practical, finite sample applications the algorithm is accurate except in the presence of large observation errors and low to a multi-modal structure in the posterior distribution over paths. More importantly, the variational approximation assisted sampling algorithm outperforms hybrid Monte Carlo in terms of computational efficiency, except when the diffusion process is densely observed with small errors in which case both algorithms are equally efficient. © 2011 Springer-Verlag.

A new variational radial basis function approximation for inference in multivariate diffusions

In this paper we present a radial basis function based extension to a recently proposed variational algorithm for approximate inference for diffusion processes. Inference, for state and in particular (hyper-) parameters, in diffusion processes is a challenging and crucial task. We show that the new radial basis function approximation based algorithm converges to the original algorithm and has beneficial characteristics when estimating (hyper-)parameters. We validate our new approach on a nonlinear double well potential dynamical system.

Wear and corrosion of mechanical seals

Mechanical seals are used extensively to seal machinery such as pumps, mixers and agitators in the oil, petrochemical and chemical industries. The performance of such machinery is critically dependent on these devices. Seal failures may result in the escape of dangerous chemicals, possibly causing injury or loss of life. Seal performance is limited by the choice of face materials available. These range from cast iron and stellited stainless steel to cemented and silicon carbides. The main factors that affect seal performance are the wear and corrosion of seal faces. This research investigated the feasibility of applying surface coating/treatments to seal materials, in order to provide improved seal performance. Various surface coating/treatment methods were considered; these included electroless nickel plating, ion plating, plasma nitriding, thermal spraying and high temperature diffusion processes. The best wear resistance, as evaluated by the Pin-on-Disc wear test method, was conferred by the sprayed tungsten carbide/nickel/tungsten-chromium carbide deposit, produced by the high energy plasma spraying (Jet-Kote) process. In general, no correlation was found between hardness and wear resistance or surface finish and friction. This is due primarily to the complexity of the wear and frictional oxidation, plastic deformation, ploughing, fracture and delamination. Corrosion resistance was evaluated by Tafel extrapolation, linear polarisation and anodic potentiodynamic polarisation techniques. The best corrosion performance was exhibited by an electroless nickel/titanium nitride duplex coating due to the passivity of the titanium nitride layer in the acidified salt solution. The surface coating/treatments were ranked using a systematic method, which also considered other properties such as adhesion, internal stress and resistance to thermal cracking. The sealing behaviour of surface coated/treated seals was investigated on an industrial seal testing rig. The best sealing performances were exhibited by the Jet-Kote and electroless nickel silicon carbide composite coated seals. The failure of the electroless nickel and electroless nickel/titanium nitride duplex coated seals was due to inadequate adhesion of the deposits to the substrate. Abrasion of the seal faces was the principal wear mechanism. For operation in an environment similar to the experimental system employed (acidified salt solution) the Jet-Kote deposit appears to be the best compromise.

Networking - a statistical physics perspective

Networking encompasses a variety of tasks related to the communication of information on networks; it has a substantial economic and societal impact on a broad range of areas including transportation systems, wired and wireless communications and a range of Internet applications. As transportation and communication networks become increasingly more complex, the ever increasing demand for congestion control, higher traffic capacity, quality of service, robustness and reduced energy consumption requires new tools and methods to meet these conflicting requirements. The new methodology should serve for gaining better understanding of the properties of networking systems at the macroscopic level, as well as for the development of new principled optimization and management algorithms at the microscopic level. Methods of statistical physics seem best placed to provide new approaches as they have been developed specifically to deal with nonlinear large-scale systems. This review aims at presenting an overview of tools and methods that have been developed within the statistical physics community and that can be readily applied to address the emerging problems in networking. These include diffusion processes, methods from disordered systems and polymer physics, probabilistic inference, which have direct relevance to network routing, file and frequency distribution, the exploration of network structures and vulnerability, and various other practical networking applications. © 2013 IOP Publishing Ltd.