| A practical guide to structured expert elicitation using the IDEA protocol V Hemming, MA Burgman, AM Hanea, MF McBride, BC Wintle Methods in Ecology and Evolution 9 (1), 169-180, 2018 | 445 | 2018 |
| What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis? BG Marcot, AM Hanea Computational Statistics 36 (3), 2009-2031, 2021 | 359 | 2021 |
| Hybrid method for quantifying and analyzing Bayesian belief nets AM Hanea, D Kurowicka, RM Cooke Quality and Reliability Engineering International 22 (6), 709-729, 2006 | 162 | 2006 |
| I nvestigate D iscuss E stimate A ggregate for structured expert judgement AM Hanea, MF McBride, MA Burgman, BC Wintle, F Fidler, L Flander, ... International Journal of Forecasting 33 (1), 267-279, 2017 | 127 | 2017 |
| Non-parametric Bayesian networks: Improving theory and reviewing applications A Hanea, OM Napoles, D Ababei Reliability Engineering & System Safety 144, 265-284, 2015 | 126 | 2015 |
| Expert judgement for dependence in probabilistic modelling: A systematic literature review and future research directions C Werner, T Bedford, RM Cooke, AM Hanea, O Morales-Napoles European Journal of Operational Research 258 (3), 801-819, 2017 | 116 | 2017 |
| A Bayesian network approach for coastal risk analysis and decision making WS Jäger, EK Christie, AM Hanea, C Den Heijer, T Spencer Coastal Engineering 134, 48-61, 2018 | 109 | 2018 |
| Eliciting improved quantitative judgements using the IDEA protocol: A case study in natural resource management V Hemming, TV Walshe, AM Hanea, F Fidler, MA Burgman PloS one 13 (6), e0198468, 2018 | 107 | 2018 |
| Classical meets modern in the IDEA protocol for structured expert judgement AM Hanea, MF McBride, MA Burgman, BC Wintle Journal of Risk Research 21 (4), 417-433, 2018 | 87 | 2018 |
| Mining and visualising ordinal data with non-parametric continuous BBNs AM Hanea, D Kurowicka, RM Cooke, DA Ababei Computational Statistics & Data Analysis 54 (3), 668-687, 2010 | 76 | 2010 |
| Expert judgement in risk and decision analysis AM Hanea, GF Nane, T Bedford, S French Springer, 2021 | 65 | 2021 |
| Towards BBN based risk modelling of process plants B Ale, C Van Gulijk, A Hanea, D Hanea, P Hudson, PH Lin, S Sillem Safety Science 69, 48-56, 2014 | 60 | 2014 |
| Uncertainty quantification with experts: Present status and research needs AM Hanea, V Hemming, GF Nane Risk Analysis 42 (2), 254-263, 2022 | 59 | 2022 |
| The value of performance weights and discussion in aggregated expert judgments AM Hanea, MF McBride, MA Burgman, BC Wintle Risk Analysis 38 (9), 1781-1794, 2018 | 52 | 2018 |
| Bayesian network modeling and expert elicitation for probabilistic eruption forecasting: Pilot study for Whakaari/White Island, New Zealand A Christophersen, NI Deligne, AM Hanea, L Chardot, N Fournier, ... Frontiers in Earth Science 6, 211, 2018 | 51 | 2018 |
| Algorithms for non-parametric Bayesian belief nets AM Hanea | 47 | 2008 |
| Bayesian networks elucidate interactions between fire and other drivers of terrestrial fauna distributions BA Hradsky, TD Penman, D Ababei, A Hanea, EG Ritchie, A York, ... Ecosphere 8 (8), e01926, 2017 | 46 | 2017 |
| Weighting and aggregating expert ecological judgments V Hemming, AM Hanea, T Walshe, MA Burgman Ecological Applications 30 (4), e02075, 2020 | 39 | 2020 |
| IDEA for uncertainty quantification AM Hanea, M Burgman, V Hemming Elicitation: The science and art of structuring judgement, 95-117, 2018 | 28 | 2018 |
| Experimental results about the assessments of conditional rank correlations by experts: Example with air pollution estimates O Morales-Nápoles, AM Hanea, DTH Worm shers, 2014 | 28 | 2014 |
Mark BurgmanProfessor of Risk Analysis and Environmental Policy, Imperial College Londonimperial.ac.uk의 이메일 확인됨