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This study demonstrates how Bayes linear methodologies developed at Durham University have impacted on industrial practice. Two examples are given. The approach has been applied by London Underground Ltd. to the management of bridges, stations and other civil engineering assets, enabling a whole-life strategic approach to maintenance and renewal to reduce costs and increase safety. The approach has won a major award for innovation in engineering and technology. The methodology has also been applied by Unilever and Fera to improve methods of assessing product safety and in particular the risk of chemical ingredients in products causing allergic skin reactions.
The petrochemical industry is eager to develop advanced fuels which improve fuel efficiency both for economic and environmental reasons. Statistics plays a crucial role in this costly process. Innovative Bayesian methodology developed by Gilmour was applied at Shell Global Solutions to data from fuel experiments to solve a recurring statistical problem. The usefulness of this approach to the wider petrochemical industry has been recognized by the industry-based Coordinating European Council (CEC) for the Development of Performance Tests for Fuels, Lubricants and other Fluids, who in their statistics manual have included Gilmour's method as an alternative to procedures in the ISO 5725 standard.
In response to the deficiencies in bank risk management revealed following the 2008 financial crisis, one of the mandated requirements under the Basel III regulatory framework is for banks to backtest the internal models they use to price their assets and to calculate how much capital they require should a counterparty default. Qiwei Yao worked with the Quantitative Analyst — Exposure team at Barclays Bank, which is responsible for constructing the Barclays Counterpart Credit Risk (CCR) backtesting methodology. They made use of several statistical methods from Yao's research to construct the newly developed backtesting methodology which is now in operation at Barclays Bank. This puts the CCR assessment and management at Barclays in line with the Basel III regulatory capital framework.
Newcastle University's fundamental research into the automated synthesis of asynchronous systems and metastability analysis has resulted in new technologies that have been adopted worldwide by the microprocessor industry and educational sectors. In particular, Newcastle's asynchronous design methods and tools based on Petri nets have been used by the industry leading vendor Intel Corporation for their switch silicon technology, on which most transactions on the NYSE and NASDAQ (with combined daily volume of trade exceeding £80 billion) now rely. Oracle Corporation used the results of Newcastle's metastability analysis research for building their SPARC series of servers, marketed as having "world's fastest microprocessor".
This impact case study is based on a Knowledge Transfer Partnership (KTP) between the School of Mathematics, Statistics and Actuarial Science, University of Kent and KROHNE Ltd, a world leading manufacturer of industrial measuring instruments. These precision instruments (typically flow meters and density meters) need to be calibrated accurately before being used and this is an expensive and time-consuming process.
The purpose of the KTP was to use Bayesian methodology developed by Kent statisticians to establish a novel calibration procedure that improves on the existing procedure by incorporating historical records from calibration of previous instruments of the same type. This reduces substantially the number of test runs needed to calibrate a new instrument and will increase capacity by up to 50%.
The impact of the KTP, which was graded as `Outstanding', has been to change the knowledge and capability of the Company, so that they can improve the performance of their manufacturing process by implementing this novel calibration method. This has been achieved by adapting the underpinning Kent research to the specific context of the calibration problem, by running many calibrations to demonstrate the effectiveness of the method in practice, and by supporting the implementation of the new calibration method within the Company's core software.
Moreover, the project has changed the Company's thinking on fundamental science, particularly industrial mathematics. The value of historical data, and the usefulness of Bayesian methods, is now widely appreciated and training for staff in Bayesian Statistics is being introduced. Thus the project has not only changed the protocols of the Company, it has also changed their practice.
The WinBUGS software (and now OpenBUGS software), developed initially at Cambridge from 1989-1996 and then further at Imperial from 1996-2007, has made practical MCMC Bayesian methods readily available to applied statisticians and data analysts. The software has been instrumental in facilitating routine Bayesian analysis of a vast range of complex statistical problems covering a wide spectrum of application areas, and over 20 years after its inception, it remains the leading software tool for applied Bayesian analysis among both academic and non-academic communities internationally. WinBUGS had over 30,000 registered users as of 2009 (the software is now open-source and users are no longer required to register) and a Google search on the term `WinBUGS' returns over 205,000 hits (over 42,000 of which are since 2008) with applications as diverse as astrostatistics, solar radiation modelling, fish stock assessments, credit risk assessment, production of disease maps and atlases, drug development and healthcare provider profiling.
Designs for complex structures like cars, aeroplanes and modern buildings suffer from unpredictable vibrations that lead to anything from irritating noises to dangerous structural failures. Predicting the distribution of vibrational energy in large coupled systems is an important and challenging task of major interest to industry. Until recently there was no reliable method to predict vibrations at the important mid-to-high frequency ranges.
There is a need to gain accurate predictions of vibrations at the design stage. However, previous techniques developed in the context of Quantum Chaos are too cumbersome to be used in a fast-moving commercial design setting. Bandtlow has used his expertise to develop a novel method that computes a very close approximation to these predictions but in a reasonable time. Bandtlow's method of constructing an efficient mathematical model for spectral vibrations has informed inuTech's latest product and led to enhanced performance of automobiles and aircraft.
A generalized additive model (GAM) explores the extent to which a single output variable of a complex system in a noisy environment can be described by a sum of smooth functions of several input variables.
Bath research has substantially improved the estimation and formulation of GAMs and hence
This improved statistical infrastructure has resulted in improved data analysis by practitioners in fields such as natural resource management, energy load prediction, environmental impact assessment, climate policy, epidemiology, finance and economics. In REF impact terms, such changes in practice by practitioners leads ultimately to direct economic and societal benefits, health benefits and policy changes. Below, these impacts are illustrated via two specific examples: (1) use of the methods by the energy company EDF for electricity load forecasting and (2) their use in environmental management. The statistical methods are implemented in R via the software package mgcv, largely written at Bath. As a `recommended' R package mgcv has also contributed to the global growth of R, which currently has an estimated 1.2M business users worldwide [A].
In a series of papers published from 1999 on, Aitken (Maxwell Institute) and collaborators applied Bayesian statistics to develop a methodology for the quantification of judicial evidence derived from forensic analyses. They proposed and implemented procedures for (i) determining the optimal size of samples that should be taken from potentially incriminating material (such as drugs seized); and (ii) the estimation of likelihood ratios characterising evidence provided by multivariate hierarchical data (such as the chemical composition of crime-scene samples). Their procedures have been recommended in international guideline documents (including a 2009 publication by the United Nations Office on Drugs and Crime) and have been routinely used by forensic science laboratories worldwide since 2008. The research has therefore had an impact on the administration of justice, leading to a better use of evidence and accompanying judicial and economic benefits. Examples are given from laboratories in Australia, Sweden and The Netherlands.
Statistical research undertaken at Sheffield has resulted in the provision of internationally-agreed calibration curves for radiocarbon dating that offer greater accuracy and higher resolution, and which (for the first time) span the full range of timelines over which radiocarbon dating is feasible. Since the amount of radioactive carbon in the Earth's atmosphere has not remained constant over time, anyone seeking to interpret a radiocarbon determination now calibrates it using one of these curves, which results in up to 50% reduction in calibrated date intervals over those previously obtainable. Non-academic users of these curves include staff in commercial radiocarbon laboratories, those working in commercial archaeology units, freelance archaeological consultants, palaeoenvironmental scientists working in governmental and intergovernmental bodies, private and public sector staff charged with the care of ancient buildings and environments, and freelance consultants who undertake radiocarbon dating in order to advise private customers, public sector companies and government agencies.