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DOI10.1016/j.envsoft.2018.09.016
Advances in Bayesian network modelling: Integration of modelling technologies
Marcot, Bruce G.1; Penman, Trent D.2
发表日期2019
ISSN1364-8152
EISSN1873-6726
卷号111页码:386-393
英文摘要

Bayesian network (BN) modeling is a rapidly advancing field. Here we explore new methods by which BN model development and application are being joined with other tools and model frameworks. Advances include improving areas of Bayesian classifiers and machine-learning algorithms for model structuring and parameterization, and development of time-dynamic models. Increasingly, BN models are being integrated with: management decision networks; structural equation modeling of causal networks; Bayesian neural networks; combined discrete and continuous variables; object-oriented and agent-based models; state-and-transition models; geographic information systems; quantum probability; and other fields. Integrated BNs (IBNs) are becoming useful tools in risk analysis, risk management, and decision science for resource planning and environmental management. In the near future, IBNs may become self-structuring, self-learning systems fed by real-time monitoring data. Such advances may make model validation difficult, and may question model credibility, particularly if based on uncertain sources of knowledge systems and big data.


WOS研究方向Computer Science ; Engineering ; Environmental Sciences & Ecology
来源期刊ENVIRONMENTAL MODELLING & SOFTWARE
文献类型期刊论文
条目标识符http://gcip.llas.ac.cn/handle/2XKMVOVA/91668
作者单位1.US Forest Serv, USDA, Portland, OR 97204 USA;
2.Univ Melbourne, Sch Ecosyst & Forest Sci, Melbourne, Vic, Australia
推荐引用方式
GB/T 7714
Marcot, Bruce G.,Penman, Trent D.. Advances in Bayesian network modelling: Integration of modelling technologies[J],2019,111:386-393.
APA Marcot, Bruce G.,&Penman, Trent D..(2019).Advances in Bayesian network modelling: Integration of modelling technologies.ENVIRONMENTAL MODELLING & SOFTWARE,111,386-393.
MLA Marcot, Bruce G.,et al."Advances in Bayesian network modelling: Integration of modelling technologies".ENVIRONMENTAL MODELLING & SOFTWARE 111(2019):386-393.
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