An application of the Bayesian approach to stock assessment model uncertainty

被引:22
|
作者
Hammond, TR [1 ]
O'Brien, CM [1 ]
机构
[1] CEFAS Lowestoft Labs, Lowestoft NR33 OHT, Suffolk, England
关键词
decision analysis; Bayesian networks; model uncertainty; ecosystem effects; fisheries management;
D O I
10.1006/jmsc.2001.1051
中图分类号
S9 [水产、渔业];
学科分类号
0908 ;
摘要
Bayesian methods hav e a number of advantages that make them especially useful in the provision of fisheries management advice: they permit formal decision analysis. and they facilitate the incorporation of model uncertainty. The latter may be particularly useful in the management of contentious fisheries. where different nations and interest groups mag suggest alternative assessment models and management each likely to imply different findings, even when using the same data. Such situations might he approached in a number of different ways. For example. one might attempt to choose a best model from all those available and to base decisions on it alone, Alternatively. one might make decisions that lead to acceptable outcomes under all envisaged models: or one could reach decisions that are good on average (where average is taken over the set of all competing models and is weighted by a measure of how well each model coheres with available information). This last approach is advocated in this paper. and a Bayesian technique for achieving it is presented and discussed. The main points of the paper are illustrated with a hypothetical application of the technique to the rebuilding of the biomass of haddock by a selective culling of seals.
引用
收藏
页码:648 / 656
页数:9
相关论文
共 50 条
  • [41] An efficient Bayesian uncertainty quantification approach with application to k-ω-γ transition modeling
    Zhang, Jincheng
    Fu, Song
    COMPUTERS & FLUIDS, 2018, 161 : 211 - 224
  • [42] Accounting for Disease Model Uncertainty in Mapping Heterogeneous Traits - Bayesian Model Averaging Approach
    Biswas, Swati
    Papachristou, Charalampos
    HUMAN HEREDITY, 2010, 69 (04) : 242 - 253
  • [43] Bayesian assessment of uncertainty in metrology: a tutorial
    Lira, I.
    Grientschnig, D.
    METROLOGIA, 2010, 47 (03) : R1 - R14
  • [44] Incorporating model uncertainty in cost-effectiveness analysis:: A Bayesian model averaging approach
    Negrin, Miguel A.
    Vazquez-Polo, Francisco-Jose
    JOURNAL OF HEALTH ECONOMICS, 2008, 27 (05) : 1250 - 1259
  • [45] An Integrated Approach to Uncertainty Assessment for Coalbed Methane Model
    Yang, Yong
    Zhang, Ming
    Bie, Aifang
    Cui, Zehong
    Xia, Zhaohui
    PROCEEDINGS OF THE INTERNATIONAL FIELD EXPLORATION AND DEVELOPMENT CONFERENCE 2017, 2019, : 1560 - 1567
  • [46] The application of Bayesian network in Uncertainty management
    颜诗洋
    无线互联科技, 2013, (08) : 173 - 174
  • [47] Evaluation and uncertainty assessment of wheat yield prediction by multilayer perceptron model with bayesian and copula bayesian approaches
    Bazrafshan, Ommolbanin
    Ehteram, Mohammad
    Moshizi, Zahra Gerkaninezhad
    Jamshidi, Sajad
    AGRICULTURAL WATER MANAGEMENT, 2022, 273
  • [48] A pragmatic Bayesian approach to predictive uncertainty
    Murray, Iain
    Snelson, Edward
    MACHINE LEARNING CHALLENGES: EVALUATING PREDICTIVE UNCERTAINTY VISUAL OBJECT CLASSIFICATION AND RECOGNIZING TEXTUAL ENTAILMENT, 2006, 3944 : 33 - 40
  • [49] Bayesian Monte Carlo and maximum likelihood approach for uncertainty estimation and risk management: Application to lake oxygen recovery model
    Chaudhary, Abhishek
    Hantush, Mohamed M.
    WATER RESEARCH, 2017, 108 : 301 - 311
  • [50] Uncertainty in pose estimation: A Bayesian approach
    Callari, FG
    Soucy, G
    Ferrie, FP
    FOURTEENTH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1 AND 2, 1998, : 972 - 976