Stone, A. and Gosling, J.P. (2025). Binary AddiVortes: (Bayesian) Additive Voronoi tessellations for binary classification with an application to predicting home mortgage application outcomes.
Stone, A. and Gosling, J.P. (2025). H-AddiVortes: Heteroscedastic (Bayesian) Additive Voronoi Tessellations.
Stone, A. and Gosling, J.P. (2025). AddiVortes: (Bayesian) additive Voronoi tessellations. Journal of Computational and Graphical Statistics, 34, 859-71.
Pina-Sánchez, J., Dhami, M. and Gosling, J.P. (2024). What are the main characteristics determining sentence severity? An empirical exploration of shoplifting offences using spike-and-slab models Chapter 30, the Research Handbook of Judicial Politics.
Vernon, I. and Gosling, J.P. (2023). A Bayesian computer model analysis of robust Bayesian analyses. Bayesian Analysis, 18, 1367-99.
Pina-Sánchez, J. and Gosling, J.P. (2022). Enhancing the measurement of sentence severity through expert knowledge elicitation. Journal of Legal Research Methodology, 2, 26-45.
Holzhauer, B., Hampson, L.V., Gosling, J.P., Bornkamp, B., Kahn, J., Lange, M.R, Luo, W.L, Brindicci, C., Lawrence, D., Ballerstedt, S. and O'Hagan, A. (2022). Eliciting judgements about dependent quantities of interest: the SHELF extension and copula methods illustrated using an asthma case study. Pharmaceutical Statistics, 21, 1005-21.
Pope, C., Gosling, J.P., Barber, S., Johnson, J., Yamaguchi, T., Feingold, G. and Blackwell, P.G. (2021). Gaussian Process Modeling of Heterogeneity and Discontinuities Using Voronoi Tessellations. Technometrics, 63, 53-63.
Pina-Sánchez, J. and Gosling, J.P. (2020). Tackling selection bias in sentencing data analysis: a new approach based on a scale of severity. Quality and Quantity, 54, 1047-73.
Punt, A., Firman, J., Boobis, A., Cronin, M., Gosling, J.P., Wilks, M., Hepburn, P., Thiel, A. and Fussell, K (2020). Potential of ToxCast data in the safety assessment of food chemicals. Toxicological Sciences, 174, 326-340.
Colson, A., Bolger, F., French, S., Frewer, L., Gosling, J. P., Hart, A., Quigley, J. and Rowe, G. (2020). Training courses on expert knowledge elicitation. EFSA Supporting Publications, 17, 1710E.
Wittwehr, C., Blomstedt, P., Gosling, J.P., Peltola, T., Raffael, B., Richarz, A., Sienkiewicz, M., Whaley, P., Worth, A. and Whelan, M. (2020). Artificial intelligence for chemical risk assessment. Computational Toxicology, 13, 100114.
Thresher, A., Gosling, J.P. and Williams, R. (2019). Generation of TD50 values for carcinogenicity study data. Toxicology Research, 8, 696-703 .
Manderson, A., Rayson, M., Cripps, E., Girolami, M., Gosling, J.P., Hodkiewicz, M., Jones, N. and Ivey, G. (2019). Uncertainty quantification of density and stratification estimates with implications for predicting ocean dynamics. Journal of Atmospheric and Oceanic Technology, 36, 1313-30.
Wicks, K., Stretton, C., Popple, A., Williams, J., Maxwell, G., Gosling, J.P., Kimber, I. and Dearman, R.J. (2019). T lymphocyte phenotype of contact allergic patients: experience with nickel and paraphenylene diamine. Contact Dermatitis, 81, 43-53.
Gosling, J.P. (2019). The importance of mathematical modelling in chemical risk assessment and the associated quantification of uncertainty. Computational Toxicology, 10, 44-50.
Pina-Sánchez, J., Gosling, J.P., Chung, H., Geneletti, S., Bourgeois, E. and Marder, I. (2019). Have the England and Wales guidelines influenced sentencing severity? An empirical analysis using a scale of severity and time-series analyses. British Journal of Criminology, 59, 979-1001.
Astfalck, L., Cripps, E., Gosling, J.P. and Milne, I. (2019). Emulation of vessel motion simulators for computationally efficient uncertainty quantification. Ocean Engineering, 172, 726-36.
Laing, K., Thwaites, P. and Gosling, J.P. (2019). Rank pruning for dominance queries in CP-Nets. Journal of Artificial Intelligence Research, 64, 55-107.
Paini, A. et al. (2019). New Approach Methodology - Physiologically Based Kinetic (NAM-PBK) models in support of regulatory decision making – report of an EURL ECVAM workshop. Computational Toxicology, 9, 61-72.
Andrade, J.A.A. and Gosling, J.P. (2018). Expert knowledge elicitation using item response theory. Journal of Applied Statistics, 45, 2981–98.
Dessai, S., Bhave, A., Birch, C., Conway, D., Garcia-Carreras, L., Gosling, J.P., Mittal, N. and Stainforth, D. (2018). Building narratives to characterise uncertainty in regional climate change through expert elicitation. Environmental Research Letters, 13(7).
Astfalck, L., Cripps, E., Gosling, J.P., Hodkiewicz, M. and Milne, I. (2018). Expert elicitation of directional metocean constituents. Ocean Engineering, 161, 268-76.
Gosling, J.P. (2018). SHELF: the Sheffield elicitation framework. In Elicitation: The science and art of structuring judgement (Eds. Dias et al.) (Chapter 4). Springer: New York.
Gusnanto, A., Gosling, J.P. and Pope, C. (2017). Identification of transcript regulatory patterns in cell differentiation. Bioinformatics, 33, 3235-42.
Mistry, P., Neagu, D., Sanchez-Ruiz, A., Trundle, P.R., Vessey, J.D. and Gosling, J.P. (2017). Prediction of the effect of formulation on the toxicity of chemicals. Toxicology Research, 6, 42-53.
Chu, H., Chan, S.W., Gosling, J.P., Blanchard, N., Lythe, G., Shastri, N., Molina-Paris, C. and Robey, E. (2016). Continuous effector CD8+ T Cell production in a controlled persistent infection is sustained by a proliferative intermediate population. Immunity, 45, 159-71.
Tennant, D. and Gosling, J.P. (2015). Modelling consumer intake of vegetable oils and fats. Food Additives and Contaminants, 32, 1397-405.
Johnson, J., Cui, Z., Lee, L., Gosling, J.P., Blyth, A. and Carslaw, K. (2015). Evaluating uncertainty in convective cloud microphysics using statistical emulation. Journal of Advances in Modeling Earth Systems, 7, 162-87.
Boukouvalas, A., Gosling, J.P. and Maruri-Aguilar, H. (2014). An efficient screening method for computer experiments. Technometrics, 56, 422-31.
Boobis, A., Flari, V., Gosling, J.P., Hart, A., Craig, P., Rushton, L. and Idahosa-Taylor, E. (2013). Interpretation of the margin of exposure for genotoxic carcinogens - elicitation of expert knowledge about the form of the dose response curve at human relevant exposures. Food and Chemical Toxicology, 57, 106-18.
Gosling, J.P., Hart, A., Owen, H., Davies, M., Li, J. and MacKay, C. (2013). A Bayes linear approach to weight-of-evidence risk assessment for skin allergy. Bayesian Analysis, 8, 169-86.
Truong, P.N., Heuvelink, G.B.M. and Gosling, J.P. (2012). Web-based tool for expert elicitation of the variogram. Computers & Geosciences, 51, 390-9.
Troffaes, M.C.M. and Gosling, J.P. (2012). Robust detection of exotic infectious diseases in animal herds: A comparative study of three decision methodologies under severe uncertainty. International Journal of Approximate Reasoning, 53, 1271-81.
Gosling, J.P., Hart, A., Mouat, D., Sabirovic, M., Scanlon, S. and Simmons, A. (2012). Quantifying experts' uncertainty about the future cost of exotic diseases. Risk Analysis, 32, 881-93.
Johnson, J.S., Gosling, J.P. and Kennedy, M.C. (2011). Gaussian process emulation for second-order Monte Carlo simulations. Journal of Statistical Planning and Inference, 141, 1838-48.
Andrade, J.A.A. and Gosling, J.P. (2011). Predicting rainy seasons: quantifying the beliefs of prophets. Journal of Applied Statistics, 38, 183-93.
Conti, S., Gosling, J.P., Oakley, J.E. and O'Hagan, A. (2009). Gaussian process emulation of dynamic computer codes. Biometrika, 96, 663-76.
Kennedy, M.C., Anderson, C., O'Hagan, A., Lomas, M., Woodward, F.I., Gosling, J.P. and Heinemeyer, A. (2008). Quantifying uncertainty in the biospheric carbon flux for England and Wales. Journal of the Royal Statistical Society, Series A, 171, 109-35.
Gosling, J.P., Oakley, J.E. and O'Hagan, A. (2007). Nonparametric elicitation for heavy-tailed prior distributions. Bayesian Analysis, 2, 693-718.
Oakley et al. (2025). TSD26: Expert elicitation for long-term survival outcomes. Decision Support Unit technical support document commissioned by the National Institute for Health and Care Excellence (NICE), University of Sheffield.
Gosling, J.P. and Pearman, A. (2014). Decision making under uncertainty: methods to value systemic resilience and passive provision. iBUILD report for IUK. University of Leeds.
Jones, G. and Gosling, J.P. (2013). Study on farm assurance scheme membership and compliance with regulation under cross compliance. Final report for Defra. Food and Environment Research Agency.
Hart, A., Gosling, J.P., Boobis, A., Coggon, D., Craig, P. and Jones, D. (2010). Development of a framework for evaluation and expression of uncertainties in hazard and risk assessment. Final report to the Food Standards Agency, Project Number T01056.
Gosling, J.P. and Hart, A. (2010). Technical annexe for disease cost-sharing project. Annex 8 of Draft Animal Health Bill, Defra.
Gosling, J.P. and Hart, A. (2010). Improving the change probabilities of the "Farming in 2020" model through expert elicitation. Final report for Defra. Food and Environment Research Agency.
Gosling, J.P., Hart, A., Brown, C. and Levy, L. (2009). Uncertainties in assessing risks to human health from contaminated land. Appendix 2 of Final report for Defra. Food and Environment Research Agency.
Boatman, N., Gosling, J.P. and Ramwell, C. (2009). Quantifying the environmental impacts of the campaign for the farmed environment. Final report for Defra. Food and Environment Research Agency.
Boatman, N. and Gosling, J.P. (2009). Estimating the quantitative environmental impacts of a package of potential set-aside mitigation options. Phase 2. Quantifying benefits and uncertainty: report of expert elicitation workshops. Final report for Defra. Central Science Laboratory.
Gosling, J.P. and Hart, A. (2008). Methods of addressing variability and uncertainty for improved pesticide risk assessments for non-target invertebrates: How sensitive are the standard test species? Final report for Defra, Project PS2307. Central Science Laboratory.
Gosling, J.P., Krishnan, S.M., Lythe, G., Chain, B., MacKay, C. and Molina-Paris, C. (2020). A mathematical study of CD8+ T cell responses calibrated with human data.
Wicks, K., Ronel, T., Singleton, H., Harries, M., Williams, J., Maxwell, G., Gosling, J.P., Chain, B., Kimber, I. and Dearman, R.J. (2020). Topical immunotherapy for alopecia areata: can T-cell characteristics help predict treatment success?.
Gosling, J.P. (2014). Methods for eliciting expert opinion to inform health technology assessment. Report for the MRC.
Gosling, J.P. (2014). How do we boost confidence in using mathematical modelling for toxicological safety assessments? NC3Rs Blog.
Gosling, J.P., MacKay, C., Hart, A., Owen, H., Davies, M., Safford, R., Gilmour, N., Aleksic, M., Aptula, A. and Li, J. (2014). Using subjective judgements to build Bayesian belief networks for toxicological risk assessment with an application to skin sensitisation hazard assessment. Technical report, School of Mathematics, University of Leeds.
Gosling, J.P. (2012). Comment on Article by Albert et al.. Bayesian Analysis, 7, 537-540.
Hickey, G.L., Craig, P.S., Marshall, S.J., Price, O.R., Smit, M., Chapman, P.F., Hart, A., Gosling, J.P., Luttik, R., Galay-Burgos, M. and Hamer, M. (2011). A statistical critique of the U.S. EPA's interspecies correlation estimation programme. Technical report, School of Mathematics, Durham University.
Troffaes, M.C.M. and Gosling, J.P. (2011). Robust detection of exotic infectious diseases in animal herds: A comparative study of two decision methodologies under severe uncertainty. In the proceedings of the 7th International Symposium on Imprecise Probability: Theories and Applications, Innsbruck, Austria, 2011.
Hart, A., Gosling, J.P. and Craig, P.S. (2010). A simple, structured approach to assessing uncertainties that are not part of a quantitative assessment. Technical report, The Food and Environment Research Agency.
Gosling, J.P. (2008). On the elicitation of continuous, symmetric, unimodal distributions. Research Report No. 577/08. Department of Probability and Statistics, The University of Sheffield. Unpublished note.
Gosling, J.P. and O'Hagan, A. (2007). Understanding the uncertainty in the biospheric carbon flux for England and Wales. Research Report No. 567/06. Department of Probability and Statistics, The University of Sheffield. This paper is a supporting document for Kennedy et al. (2008).
Gosling, J.P. (2006). Differences between estimates of expected computer code output with GEM-SA. Technical Report. Department of Probability and Statistics, The University of Sheffield. [associated data]
Gosling, J.P. (2005). Elicitation: a nonparametric view. PhD Thesis. Department of Probability and Statistics, The University of Sheffield.