Bayes and the Law: Cambridge event and new review paper

When we set up the Bayes and the Law network in 2012 we made the following assertion:

Proper use of statistics and probabilistic reasoning has the potential to improve dramatically the efficiency, transparency and fairness of the criminal justice system and the accuracy of its verdicts, by enabling the relevance of evidence – especially forensic evidence - to be meaningfully evaluated and communicated. However, its actual use in practice is minimal, and indeed the most natural way to handle probabilistic evidence (Bayes) has generally been shunned. 

The first workshop (30th August to 2nd September 2016)  that is part of our 6-month programme "Probability and Statistics in Forensic Science" at the Issac Newton Institute of Mathematics Cambridge directly addresses the above assertion and seeks to understand the scope, limitations, and barriers of using statistics and probability in court. The Workshop brings together many of the world's leading academics and pracitioners (including lawyers) in this area. Information on the programme and how to participate can be found here.

A new review paper* "Bayes and the Law" has just been published in Annual Review of Statistics and Its Application.

This paper reviews the potential and actual use of Bayes in the law and explains the main reasons for its lack of impact on legal practice. These include misconceptions by the legal community about Bayes’ theorem, over-reliance on the use of the likelihood ratio and the lack of adoption of modern computational methods. The paper argues that Bayesian Networks (BNs), which automatically produce the necessary Bayesian calculations, provide an opportunity to address most concerns about using Bayes in the law.

*Full citation:

Fenton N.E, Neil M, Berger D, “Bayes and the Law”, Annual Review of Statistics and Its Application, Volume 3, pp51-77, June 2016 http://dx.doi.org/10.1146/annurev-statistics-041715-033428. Pre-publication version is here and the Supplementary Material is here.

Bayesian networks for Cost, Benefit and Risk Analysis of Agricultural Development Projects

Successful implementation of major projects requires careful management of uncertainty and risk. Yet, uncertainty is rarely effectively calculated when analysing project costs and benefits. In the case of major agricultural and other development projects in Africa this challenge is especially important.

A paper just published* in the journal Experts Systems with Applications presents a Bayesian network (BN) modelling framework to calculate the costs, benefits, and return on investment of a project over a specified time period, allowing for changing circumstances and trade-offs. Marianne Gadeberg and Eike Luedeling have written an overview of the work here.

The framework uses hybrid and dynamic BNs containing both discrete and continuous variables over multiple time stages. The BN framework calculates costs and benefits based on multiple causal factors including the effects of individual risk factors, budget deficits, and time value discounting, taking account of the parameter uncertainty of all continuous variables. The framework can serve as the basis for various project management assessments and is illustrated using a case study of an agricultural development project. The work was a collaboration between the World Agroforestry Centre (ICRAF), Nairobi, Kenya, the Risk Information Management Group at Queen Mary (as part of the BAYES-KNOWLEDGE project) and Agena Ltd.

*The full reference is:

Yet, B., Constantinou, A., Fenton, N., Neil, M., Luedeling, E., & Shepherd, K. (2016). "A Bayesian Network Framework for Project Cost, Benefit and Risk Analysis with an Agricultural Development Case Study" . Expert Systems with Applications, Volume 60, 30 October 2016, Pages 141–155. DOI: 10.1016/j.eswa.2016.05.005. 

Until July 2016 the full published pdf is available for free.  A permanent pre-publication pdf is available here.

See also: Can we build a better project: assessing complexities in development projects

Acknowledgements: Part of this work was performed under the auspices of EU project ERC-2013-AdG339182-BAYES_KNOWLEDGE and part under ICRAF Contract No SD4/2012/214 issued to Agena. We acknowledge support from the Water, Land and Ecosystems (WLE) program of the Consultative Group on International Agricultural Research (CGIAR).