Metrics for evaluating performance and uncertainty of Bayesian network models
Metrics for evaluating performance and uncertainty of Bayesian network models
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DOI：
10.1016/j.ecolmodel.2012.01.013
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发表时间：
2012-04-10
影响因子：
3.1
通讯作者：
Marcot, Bruce G.
中科院分区：
文献类型：
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作者：
Marcot, Bruce G.
本文提出了一套选定的现有和新的度量贝叶斯网络模型的性能和不确定性。选择现有的和新的指标进行了讨论模型的敏感性分析（方差减少、熵减少、案例文件模拟）;评估情景（影响分析）;描述模型复杂性（模型变量、链接、节点状态、条件概率和节点集团的数量）;评估预测性能（混淆表，协变量和条件概率加权混淆错误率，接收器操作特征曲线下的面积，k折交叉验证，球形回报，施瓦茨的贝叶斯信息准则、真实技能统计量、Cohen的kappa）;以及评估模型后验概率分布的不确定性（贝叶斯可信区间、后验概率确定性指数、确定性包络、Gini系数）。实例应用的指标，野生动物种群分析和管理的3个现实世界的模型。使用这些指标可以极大地提高模型的可信度，接受度和适当的应用，特别是在为管理决策提供信息时。由爱思唯尔公司出版
This paper presents a selected set of existing and new metrics for gauging Bayesian network model performance and uncertainty. Selected existing and new metrics are discussed for conducting model sensitivity analysis (variance reduction, entropy reduction, case file simulation); evaluating scenarios (influence analysis); depicting model complexity (numbers of model variables, links, node states, conditional probabilities, and node cliques); assessing prediction performance (confusion tables, covariate and conditional probability-weighted confusion error rates, area under receiver operating characteristic curves, k-fold cross-validation, spherical payoff, Schwarz' Bayesian information criterion, true skill statistic, Cohen's kappa); and evaluating uncertainty of model posterior probability distributions (Bayesian credible interval, posterior probability certainty index, certainty envelope, Gini coefficient). Examples are presented of applying the metrics to 3 real-world models of wildlife population analysis and management. Using such metrics can vitally bolster model credibility, acceptance, and appropriate application, particularly when informing management decisions. Published by Elsevier B.V.