Bayesian inference for wind field retrieval

In many problems in spatial statistics it is necessary to infer a global problem solution by combining local models. A principled approach to this problem is to develop a global probabilistic model for the relationships between local variables and to use this as the prior in a Bayesian inference procedure. We show how a Gaussian process with hyper-parameters estimated from Numerical Weather Prediction Models yields meteorologically convincing wind fields. We use neural networks to make local estimates of wind vector probabilities. The resulting inference problem cannot be solved analytically, but Markov Chain Monte Carlo methods allow us to retrieve accurate wind fields.

Efficient Bayesian inference for learning in the Ising linear perceptron and signal detection in CDMA

Efficient new Bayesian inference technique is employed for studying critical properties of the Ising linear perceptron and for signal detection in code division multiple access (CDMA). The approach is based on a recently introduced message passing technique for densely connected systems. Here we study both critical and non-critical regimes. Results obtained in the non-critical regime give rise to a highly efficient signal detection algorithm in the context of CDMA; while in the critical regime one observes a first-order transition line that ends in a continuous phase transition point. Finite size effects are also studied. © 2006 Elsevier B.V. All rights reserved.

Improved message passing for inference in densely connected systems

An improved inference method for densely connected systems is presented. The approach is based on passing condensed messages between variables, representing macroscopic averages of microscopic messages. We extend previous work that showed promising results in cases where the solution space is contiguous to cases where fragmentation occurs. We apply the method to the signal detection problem of Code Division Multiple Access (CDMA) for demonstrating its potential. A highly efficient practical algorithm is also derived on the basis of insight gained from the analysis. © EDP Sciences.

Bayesian inference for wind field retrieval

In many problems in spatial statistics it is necessary to infer a global problem solution by combining local models. A principled approach to this problem is to develop a global probabilistic model for the relationships between local variables and to use this as the prior in a Bayesian inference procedure. We show how a Gaussian process with hyper-parameters estimated from Numerical Weather Prediction Models yields meteorologically convincing wind fields. We use neural networks to make local estimates of wind vector probabilities. The resulting inference problem cannot be solved analytically, but Markov Chain Monte Carlo methods allow us to retrieve accurate wind fields.

The causal relationship between information and communication technology and foreign direct investment

This paper investigates the simultaneous causal relationship between investments in information and communication technology (ICT) and flows of foreign direct investment (FDI), with reference to its implications on economic growth. For the empirical analysis we use data from 23 major countries with heterogeneous economic development for the period 1976-99. Our causality test results suggest that there is a causal relationship from ICT to FDI in developed countries, which means that a higher level of ICT investment leads to an increase inflow of FDI. ICT may contribute to economic growth indirectly by attracting more FDI. Contrarily, we could not find significant causality from ICT to FDI in developing countries. Instead, we have partial evidence of opposite causality relationship: the inflow of FDI causes further increases in ICT investment and production capacity. © United Nations University 2006.

Mapping causal knowledge: how people consider their environment during meetings

Causal mapping can help managers to think through the causal influence between issues, enabling them to base a decision on a more structured consideration. Even in regular meetings, learning and the integration of knowledge from diverse stakeholders can benefit from causal mapping. Four causal mapping meetings with management teams are analysed to assess how managers thought causally about their environment when strategy-making. We found that although managers can use other views to expand their environmental knowledge, some prefer to use familiar information rather than less familiar information. Despite this preference, many managers thought systemically about a raft of related issues. We discuss our findings in the context of regular meetings and offer improvements to the facilitation of group causal mapping.

Creativity and learning through electronic group causal mapping

This paper proposes, and begins to evaluate, a format of brainstorming-type activity which aims to release the creativity of participants and encourage them to learn about a wider range of issues in more detail. The format does this through providing a two-stage brainstorming session. After the first brainstorm, participants have an opportunity to both piggy-back off other peoples ideas (i.e. create new ideas by synthesising other peoples' ideas into their own perspectives), and share causal links to build a causal map with the brainstormed ideas. Five causal mapping sessions with organisations have been analysed. Findings suggest that ideas shared when piggy-backing are often highly creative and unique for the participant who shared them. Also piggy-backing and causal linking seem to provide effective opportunities for individual learning as participants have time to reflect upon other peoples' perspectives and share their own views on those.

Approximate Bayesian techniques for inference in stochastic dynamical systems

This thesis is concerned with approximate inference in dynamical systems, from a variational Bayesian perspective. When modelling real world dynamical systems, stochastic differential equations appear as a natural choice, mainly because of their ability to model the noise of the system by adding a variant of some stochastic process to the deterministic dynamics. Hence, inference in such processes has drawn much attention. Here two new extended frameworks are derived and presented that are based on basis function expansions and local polynomial approximations of a recently proposed variational Bayesian algorithm. It is shown that the new extensions converge to the original variational algorithm and can be used for state estimation (smoothing). However, the main focus is on estimating the (hyper-) parameters of these systems (i.e. drift parameters and diffusion coefficients). The new methods are numerically validated on a range of different systems which vary in dimensionality and non-linearity. These are the Ornstein-Uhlenbeck process, for which the exact likelihood can be computed analytically, the univariate and highly non-linear, stochastic double well and the multivariate chaotic stochastic Lorenz '63 (3-dimensional model). The algorithms are also applied to the 40 dimensional stochastic Lorenz '96 system. In this investigation these new approaches are compared with a variety of other well known methods such as the ensemble Kalman filter / smoother, a hybrid Monte Carlo sampler, the dual unscented Kalman filter (for jointly estimating the systems states and model parameters) and full weak-constraint 4D-Var. Empirical analysis of their asymptotic behaviour as a function of observation density or length of time window increases is provided.

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.

A comparison of variational and Markov chain Monte Carlo methods for inference in partially observed stochastic dynamic systems

In recent work we have developed a novel variational inference method for partially observed systems governed by stochastic differential equations. In this paper we provide a comparison of the Variational Gaussian Process Smoother with an exact solution computed using a Hybrid Monte Carlo approach to path sampling, applied to a stochastic double well potential model. It is demonstrated that the variational smoother provides us a very accurate estimate of mean path while conditional variance is slightly underestimated. We conclude with some remarks as to the advantages and disadvantages of the variational smoother. © 2008 Springer Science + Business Media LLC.

Variational inference for Student-t MLP models

This paper presents a novel methodology to infer parameters of probabilistic models whose output noise is a Student-t distribution. The method is an extension of earlier work for models that are linear in parameters to nonlinear multi-layer perceptrons (MLPs). We used an EM algorithm combined with variational approximation, the evidence procedure, and an optimisation algorithm. The technique was tested on two regression applications. The first one is a synthetic dataset and the second is gas forward contract prices data from the UK energy market. The results showed that forecasting accuracy is significantly improved by using Student-t noise models.

FDI, trade and growth, a causal link?

. FDI, Trade and Growth, a Causal Link? (RP0710) Prof Nigel DRIFFIELD Dr Rakesh BISSOONDEEAL Mayang Pramadhani Non-technical Summary This paper explores the relationship between imports, exports, foreign direct investment and growth. For some time there has been a good deal of debate whether trade and foreign direct investment) FDI are substitutes and complements, with the existing literature generating some rather contradictory findings. We show, for Indonesia that inward FDI and both imports and exports are complementary, and further that FDI causes an increase in trade. This is of particular interest for a country such as Indonesia, that has attracted a high proportion of export-orientated inward investment. This, theoretically at least is associated with an increase in imports, in the form of capital goods and components, but a reduction in imports. We show that the previous literature that fails to find such a relationship does so because both trade and FDI are associated with growth, and previous work ignores these growth effects when seeking to isolate the relationship between trade and FDI.

Data assimilation for precipitation nowcasting using Bayesian inference

This work introduces a new variational Bayes data assimilation method for the stochastic estimation of precipitation dynamics using radar observations for short term probabilistic forecasting (nowcasting). A previously developed spatial rainfall model based on the decomposition of the observed precipitation field using a basis function expansion captures the precipitation intensity from radar images as a set of ‘rain cells’. The prior distributions for the basis function parameters are carefully chosen to have a conjugate structure for the precipitation field model to allow a novel variational Bayes method to be applied to estimate the posterior distributions in closed form, based on solving an optimisation problem, in a spirit similar to 3D VAR analysis, but seeking approximations to the posterior distribution rather than simply the most probable state. A hierarchical Kalman filter is used to estimate the advection field based on the assimilated precipitation fields at two times. The model is applied to tracking precipitation dynamics in a realistic setting, using UK Met Office radar data from both a summer convective event and a winter frontal event. The performance of the model is assessed both traditionally and using probabilistic measures of fit based on ROC curves. The model is shown to provide very good assimilation characteristics, and promising forecast skill. Improvements to the forecasting scheme are discussed