Good practice in Bayesian network modelling
Good practice in Bayesian network modelling
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DOI：
10.1016/j.envsoft.2012.03.012
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发表时间：
2012-11-01
影响因子：
4.9
通讯作者：
Pollino, Carmel A.
中科院分区：
文献类型：
--
作者：
Chen, Serena H.;Pollino, Carmel A.
贝叶斯网络（BN）越来越多地被用于模拟环境系统，以便：整合多个问题和系统组件;利用来自不同来源的信息;以及处理缺失数据和不确定性。BN也有一个模块化的架构，便于迭代模型开发。要使模型在产生和分享知识或提供决策支持方面具有价值，就必须采用良好的建模做法。本文提供了发展和评估环境系统的贝叶斯网络模型的指导方针，并提出了一个案例研究的幼年Astacopsis gouldi，塔斯马尼亚州的巨型淡水小龙虾的栖息地适宜性模型。这些指导方针要求明确界定模型的目标和范围，并使用系统的概念模型来形成BN的结构，该结构应简约但涵盖所有关键组成部分和流程。在定义了所有变量的状态和条件概率之后，应该通过一套定量和定性的模型评估形式来评估BN。所有的假设、不确定性、每个节点和链接的说明和推理、数据和信息来源以及评价结果都必须清楚地记录在案。遵循这些标准将使建模过程和模型本身在其特定限制范围内透明、可信和稳健。(C)2012爱思唯尔有限公司保留所有权利。
Bayesian networks (BNs) are increasingly being used to model environmental systems, in order to: integrate multiple issues and system components; utilise information from different sources; and handle missing data and uncertainty. BNs also have a modular architecture that facilitates iterative model development. For a model to be of value in generating and sharing knowledge or providing decision support, it must be built using good modelling practice. This paper provides guidelines to developing and evaluating Bayesian network models of environmental systems, and presents a case study habitat suitability model for juvenile Astacopsis gouldi, the giant freshwater crayfish of Tasmania. The guidelines entail clearly defining the model objectives and scope, and using a conceptual model of the system to form the structure of the BN, which should be parsimonious yet capture all key components and processes. After the states and conditional probabilities of all variables are defined, the BN should be assessed by a suite of quantitative and qualitative forms of model evaluation. All the assumptions, uncertainties, descriptions and reasoning for each node and linkage, data and information sources, and evaluation results must be clearly documented. Following these standards will enable the modelling process and the model itself to be transparent, credible and robust, within its given limitations. (C) 2012 Elsevier Ltd. All rights reserved.