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Bayesian computation for comparative judgement models

   School of Mathematics

   Applications accepted all year round  Competition Funded PhD Project (UK Students Only)

About the Project

Comparative judgement models have been to used to analyse a wide range of problems from predicting the outcomes of football leagues to assessing students. More recently, comparative judgement models have been used for social good causes, including measuring deprivation in developing countries and protecting endangered birds in the rain forest. By allowing respondents, known as judges, to compare objects in terms of the quantity of interest rather than give absolute assessments of that quantity, comparative judgement models can elicit reliable and informative responses. 

Bayesian computation can improve current comparative judgement models. Statistical methods can be developed to estimate the reliability of the judges and to understand if there are different types of judges. This can help provide better estimates. Methods can also be developed to understand what is causing the underlying judgements.


Seymour, R.G., Sirl, D., Preston, S.P., Dryden, I.L., Ellis, M.J.A., Perrat, B. et al. (2022) The Bayesian Spatial Bradley–Terry model: Urban deprivation modelling in Tanzania. Journal of the Royal Statistical Society: Series C (Applied Statistics), 288– 308. Available from:

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