Staff profile

• Bayesian statistics
• Expert knowledge elicitation
• Robust analyses
• Uncertainty communication
• Uncertainty quantification

Chapter in book

• Gosling, J. P. (2018). SHELF: the Sheffield elicitation framework. In Elicitation (61-93). Springer, Cham

Doctoral Thesis

• Gosling, J. P. (2005). Elicitation: a nonparametric view. (Thesis). University of Sheffield. https://durham-repository.worktribe.com/output/1647140

Journal Article

• Paini, A., Joossens, E., Bessems, J., Desalegn, A., Dorne, J.-L., Gosling, J., Heringa, M., Klaric, M., Kramer, N., Loizou, G., & others. (online). EURL ECVAM workshop on new generation of physiologically-based kinetic models in risk assessment
• Gosling, J. P. (online). Methods for eliciting expert opinion to inform health technology assessment
• Stone, A. J., & Gosling, J. P. (online). AddiVortes: (Bayesian) Additive Voronoi Tessellations. Journal of Computational and Graphical Statistics, https://doi.org/10.1080/10618600.2024.2414104
• Vernon, I., & Gosling, J. (2023). A Bayesian Computer Model Analysis of Robust Bayesian Analyses. Bayesian Analysis, 18(4), 1367-1399. https://doi.org/10.1214/22-ba1340
• Pina-Sanchez, J., & Gosling, J. P. (2022). Enhancing the Measurement of Sentence Severity through Expert Knowledge Elicitation. Journal of Legal Research Methodology, 2(1), 26-45. https://doi.org/10.19164/jlrm.v2i1.1241
• Holzhauer, B., Hampson, L. V., Gosling, J. P., Bornkamp, B., Kahn, J., Lange, M. R., Luo, W.-L., Brindicci, C., Lawrence, D., Ballerstedt, S., & others. (2022). Eliciting judgements about dependent quantities of interest: The SHeffield ELicitation Framework extension and copula methods illustrated using an asthma case study. Pharmaceutical Statistics, 21(5), 1005-1021. https://doi.org/10.1002/pst.2212
• Pope, C. A., Gosling, J. P., Barber, S., Johnson, J. S., Yamaguchi, T., Feingold, G., & Blackwell, P. G. (2021). Gaussian process modeling of heterogeneity and discontinuities using Voronoi tessellations. Technometrics, 63(1), 53-63
• Punt, A., Firman, J., Boobis, A., Cronin, M., Gosling, J. P., Wilks, M. F., Hepburn, P. A., Thiel, A., & Fussell, K. C. (2020). Potential of ToxCast data in the safety assessment of food chemicals. Toxicological Sciences, 174(2), 326-340
• Pina-Sánchez, J., & Gosling, J. P. (2020). Tackling selection bias in sentencing data analysis: a new approach based on a scale of severity. Quality and Quantity, 54(3), 1047-1073
• Wittwehr, C., Blomstedt, P., Gosling, J. P., Peltola, T., Raffael, B., Richarz, A.-N., Sienkiewicz, M., Whaley, P., Worth, A., & Whelan, M. (2020). Artificial Intelligence for chemical risk assessment. Computational Toxicology, 13,
• Manderson, A., Rayson, M., Cripps, E., Girolami, M., Gosling, J., Hodkiewicz, M., Ivey, G., & Jones, N. (2019). Uncertainty quantification of density and stratification estimates with implications for predicting ocean dynamics. Journal of Atmospheric and Oceanic Technology, 36(7), 1313-1330
• Wicks, K., Stretton, C., Popple, A., Beresford, L., Williams, J., Maxwell, G., Gosling, J. P., Kimber, I., & Dearman, R. J. (2019). T lymphocyte phenotype of contact-allergic patients: experience with nickel and p-phenylenediamine. Contact Dermatitis, 81(1), 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
• Astfalck, L., Cripps, E., Gosling, J., & Milne, I. (2019). Emulation of vessel motion simulators for computationally efficient uncertainty quantification. Ocean Engineering, 172, 726-736
• Laing, K., Thwaites, P. A., & Gosling, J. P. (2019). Rank pruning for dominance queries in CP-nets. Journal of Artificial Intelligence Research, 64, 55-107
• Pina-Sánchez, J., Gosling, J. P., Chung, H.-I., Bourgeois, E., Geneletti, S., & Marder, I. D. (2019). Have The England and Wales Guidelines Affected Sentencing Severity? An Empirical Analysis Using a Scale of Severity and Time-Series Analyses. The British Journal of Criminology: An International Review of Crime and Society, 59(4), 979-1001
• Thresher, A., Gosling, J. P., & Williams, R. (2019). Generation of TD50 values for carcinogenicity study data
• Paini, A., Leonard, J., Joossens, E., Bessems, J., Desalegn, A., Dorne, J., Gosling, J., Heringa, M., Klaric, M., Kliment, T., & others. (2019). Next generation physiologically based kinetic (NG-PBK) models in support of regulatory decision making. Computational Toxicology, 9, 61-72
• Dessai, S., Bhave, A., Birch, C., Conway, D., Garcia-Carreras, L., Gosling, J. P., Mittal, N., & Stainforth, D. (2018). Building narratives to characterise uncertainty in regional climate change through expert elicitation. Environmental Research Letters, 13(7),
• Andrade, J., & Gosling, J. (2018). Expert knowledge elicitation using item response theory. Journal of Applied Statistics, 45(16), 2981-2998
• Astfalck, L., Cripps, E., Gosling, J., Hodkiewicz, M., & Milne, I. (2018). Expert elicitation of directional metocean parameters. Ocean Engineering, 161, 268-276
• Gusnanto, A., Gosling, J. P., & Pope, C. (2017). Identification of transcript regulatory patterns in cell differentiation. Bioinformatics, 33(20), 3235-3242
• Mistry, P., Neagu, D., Sanchez-Ruiz, A., Trundle, P. R., Vessey, J. D., & Gosling, J. P. (2017). Prediction of the effect of formulation on the toxicity of chemicals
• Chu, H. H., Chan, S.-W., Gosling, J. P., Blanchard, N., Tsitsiklis, A., Lythe, G., Shastri, N., Molina-París, C., & Robey, E. A. (2016). Continuous effector CD8+ T cell production in a controlled persistent infection is sustained by a proliferative intermediate population. Immunity, 45(1), 159-171
• Tennant, D., & Gosling, J. P. (2015). Modelling consumer intakes of vegetable oils and fats
• Johnson, J., Cui, Z., Lee, L., Gosling, J., Blyth, A., & Carslaw, K. (2015). Evaluating uncertainty in convective cloud microphysics using statistical emulation. Journal of Advances in Modelling Earth Systems, 7(1), 162-187
• Boukouvalas, A., Gosling, J. P., & Maruri-Aguilar, H. (2014). An efficient screening method for computer experiments. Technometrics, 56(4), 422-431
• Gosling, J. P., Hart, A., Owen, H., Davies, M., Li, J., & MacKay, C. (2013). A Bayes Linear approach to weight-of-evidence risk assessment for skin allergy. Bayesian Analysis, 8(1), 169-186
• Boobis, A., Flari, V., Gosling, J. P., Hart, A., Craig, P., Rushton, L., & 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, 106-118
• Truong, P. N., Heuvelink, G., & Gosling, J. P. (2012). Web-based tool for expert elicitation of the variogram
• Andrade, J., & Gosling, J. (2011). Predicting rainy seasons: quantifying the beliefs of prophets. Journal of Applied Statistics, 38(1), 183-193
• Gosling, J. P., Hart, A., Mouat, D. C., Sabirovic, M., Scanlan, S., & Simmons, A. (2011). Quantifying Experts’ Uncertainty About the Future Cost of Exotic Diseases. Risk Analysis, 32, 881-93
• Hart, A., Gosling, J. P., Boobis, A., Coggon, D., Craig, P., & Jones, D. (2010). Development of a framework for evaluation and expression of uncertainties in hazard and risk assessment
• Johnson, J., Gosling, J., & Kennedy, M. (2010). Gaussian process emulation for second-order Monte Carlo simulations. Journal of Statistical Planning and Inference, 141, 1838-48
• Conti, S., Gosling, J. P., Oakley, J., & O'Hagan, A. (2009). Gaussian process emulation of dynamic computer codes. Biometrika, 96(3), 663-676
• Kennedy, M., Anderson, C., O'Hagan, A., Lomas, M., Woodward, I., Gosling, J. P., & Heinemeyer, A. (2008). Quantifying uncertainty in the biospheric carbon flux for England and Wales. Journal of the Royal Statistical Society: Series A, 171(1), 109-135
• Gosling, J. P., Oakley, J. E., & O’Hagan, A. (2007). Nonparametric elicitation for heavy-tailed prior distributions. Bayesian Analysis, 2(693-718),