Staff profile

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

Chapter in book

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

Doctoral Thesis

• Elicitation: a nonparametric view
  
  Gosling, J. P. (2005). Elicitation: a nonparametric view [Thesis]. University of Sheffield.

Journal Article

• AddiVortes: (Bayesian) Additive Voronoi Tessellations
  
  Stone, A. J., & Gosling, J. P. (2024). AddiVortes: (Bayesian) Additive Voronoi Tessellations. Journal of Computational and Graphical Statistics. Advance online publication. https://doi.org/10.1080/10618600.2024.2414104
• A Bayesian Computer Model Analysis of Robust Bayesian Analyses
  
  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
• Enhancing the Measurement of Sentence Severity through Expert Knowledge Elicitation
  
  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
• Eliciting judgements about dependent quantities of interest: The SHeffield ELicitation Framework extension and copula methods illustrated using an asthma case study
  
  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
• Gaussian process modeling of heterogeneity and discontinuities using Voronoi tessellations
  
  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.
• Artificial Intelligence for chemical risk assessment
  
  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.
• Tackling selection bias in sentencing data analysis: a new approach based on a scale of severity
  
  Pina-Sánchez, J., & Gosling, J. P. (2020). Tackling selection bias in sentencing data analysis: a new approach based on a scale of severity. Quality \& Quantity, 54(3), 1047-1073.
• Potential of ToxCast data in the safety assessment of food chemicals
  
  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.
• Generation of TD50 values for carcinogenicity study data
  
  Thresher, A., Gosling, J. P., & Williams, R. (2019). Generation of TD50 values for carcinogenicity study data. Toxicology Research, 8(5), 696-703.
• Uncertainty quantification of density and stratification estimates with implications for predicting ocean dynamics
  
  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.
• The importance of mathematical modelling in chemical risk assessment and the associated quantification of uncertainty
  
  Gosling, J. P. (2019). The importance of mathematical modelling in chemical risk assessment and the associated quantification of uncertainty. Computational Toxicology, 10, 44-50.
• Emulation of vessel motion simulators for computationally efficient uncertainty quantification
  
  Astfalck, L., Cripps, E., Gosling, J., & Milne, I. (2019). Emulation of vessel motion simulators for computationally efficient uncertainty quantification. Ocean Engineering, 172, 726-736.
• Rank pruning for dominance queries in CP-nets
  
  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.
• T lymphocyte phenotype of contact-allergic patients: experience with nickel and p-phenylenediamine
  
  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.
• Have The England and Wales Guidelines Affected Sentencing Severity? An Empirical Analysis Using a Scale of Severity and Time-Series Analyses
  
  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. British Journal of Criminology, 59(4), 979-1001.
• Next generation physiologically based kinetic (NG-PBK) models in support of regulatory decision making
  
  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.
• Building narratives to characterise uncertainty in regional climate change through expert elicitation
  
  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).
• Expert elicitation of directional metocean parameters
  
  Astfalck, L., Cripps, E., Gosling, J., Hodkiewicz, M., & Milne, I. (2018). Expert elicitation of directional metocean parameters. Ocean Engineering, 161, 268-276.
• Expert knowledge elicitation using item response theory
  
  Andrade, J., & Gosling, J. (2018). Expert knowledge elicitation using item response theory. Journal of Applied Statistics, 45(16), 2981-2998.
• EURL ECVAM workshop on new generation of physiologically-based kinetic models in risk assessment
  
  Paini, A., Joossens, E., Bessems, J., Desalegn, A., Dorne, J.-L., Gosling, J., Heringa, M., Klaric, M., Kramer, N., Loizou, G., & others. (2017). EURL ECVAM workshop on new generation of physiologically-based kinetic models in risk assessment.
• Identification of transcript regulatory patterns in cell differentiation
  
  Gusnanto, A., Gosling, J. P., & Pope, C. (2017). Identification of transcript regulatory patterns in cell differentiation. Bioinformatics, 33(20), 3235-3242.
• Prediction of the effect of formulation on the toxicity of chemicals
  
  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. Toxicology Research, 6(1), 42-53.
• Continuous effector CD8+ T cell production in a controlled persistent infection is sustained by a proliferative intermediate population
  
  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.
• Modelling consumer intakes of vegetable oils and fats
  
  Tennant, D., & Gosling, J. P. (2015). Modelling consumer intakes of vegetable oils and fats. Food Additives \& Contaminants: Part A, 32(9), 1397-1405.
• Evaluating uncertainty in convective cloud microphysics using statistical emulation
  
  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.
• Methods for eliciting expert opinion to inform health technology assessment
  
  Gosling, J. P. (2014). Methods for eliciting expert opinion to inform health technology assessment. Vignette Commissioned by the MRC Methodology Advisory Group. Medical Research Council (MRC) and National Institure for Health Research (NIHR).
• An efficient screening method for computer experiments
  
  Boukouvalas, A., Gosling, J. P., & Maruri-Aguilar, H. (2014). An efficient screening method for computer experiments. Technometrics, 56(4), 422-431.
• A Bayes Linear approach to weight-of-evidence risk assessment for skin allergy
  
  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.
• 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
  
  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.
• Web-based tool for expert elicitation of the variogram
  
  Truong, P. N., Heuvelink, G., & Gosling, J. P. (2012). Web-based tool for expert elicitation of the variogram. Computers \& Geosciences, 51, 390-99.
• Predicting rainy seasons: quantifying the beliefs of prophets
  
  Andrade, J., & Gosling, J. (2011). Predicting rainy seasons: quantifying the beliefs of prophets. Journal of Applied Statistics, 38(1), 183-193.
• Quantifying Experts’ Uncertainty About the Future Cost of Exotic Diseases
  
  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.
• Gaussian process emulation for second-order Monte Carlo simulations
  
  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.
• Development of a framework for evaluation and expression of uncertainties in hazard and risk assessment
  
  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. Final Report of Food Standards Agency Project, 1056.
• Gaussian process emulation of dynamic computer codes
  
  Conti, S., Gosling, J. P., Oakley, J., & O’Hagan, A. (2009). Gaussian process emulation of dynamic computer codes. Biometrika, 96(3), 663-676.
• Quantifying uncertainty in the biospheric carbon flux for England and Wales
  
  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.
• Nonparametric elicitation for heavy-tailed prior distributions
  
  Gosling, J. P., Oakley, J. E., & O’Hagan, A. (2007). Nonparametric elicitation for heavy-tailed prior distributions. Bayesian Analysis, 2(693-718).