On the assimilation of uncertain physical knowledge bases: Bayesian and non-Bayesian techniques
On the assimilation of uncertain physical knowledge bases: Bayesian and non-Bayesian techniques
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DOI：
10.1016/s0309-1708(02)00064-7
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发表时间：
2002-08-01
影响因子：
4.7
通讯作者：
Christakos, G
中科院分区：
文献类型：
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作者：
Christakos, G
我们研究跨空间和时间的不确定物理知识的同化，并讨论由此产生的环境推断和预测问题。 “同化”一词是指包括各种科学学科（大气数据分析和天气预报、环境污染测绘、暴露评估等）中的物理知识整合和处理活动的总体框架。选择适当的同化方法主要是一个概念建模事务，它受到所考虑的环境情况的物理和逻辑特征的支持，而不是仅仅依赖于纯粹的归纳方案和统计论证。在许多情况下，同化方法涉及条件化技术，使其能够将各种形式的特定地点数据与建模框架合并。我们区分解释性条件化（指同化建模的物理和认知特征）和形式条件化（纯粹的数学构造），并强调充分选择后者来代表前者的重要性。我们指出操作贝叶斯条件化方法相对于标准贝叶斯规则有相当大的改进。统计学习技术在科学探究的描述性/关联性水平上很有用，而解释/预测性水平则属于高级操作技术的领域。操作方法基于认识论观点，其中相关的自然过程用时空随机场表示，概率定律用物理上有意义的算子表示。这种观点的精神是使现场解与特定地点的信息（硬数据、不确定观测等）保持一致，同时完全满足一般知识来源（物理定律、原始方程、科学理论等）产生的约束。空间和时间上的自然场概率是基于演绎合理的推理推导出非贝叶斯操作条件的，这对于各种知识库都是有效的。对贝叶斯和非贝叶斯条件进行了分析和数值比较，并通过跨越各种地球科学学科的示例和应用获得了见解。操作贝叶斯条件化是同化建模的一个强大且通用的组成部分，在环境科学中具有许多应用，尽管在某些情况下，非贝叶斯条件（基于演绎逻辑和物理连接的表征）可以提供对自然法则所建议的数据同化框架的有意义的描述。 (C) 2002 Elsevier Science Ltd. 保留所有权利。
We study the assimilation of uncertain physical knowledge across space and time, and we discuss the resulting issues of environmental inference and prediction. The term "assimilation" refers to a general framework that includes physical knowledge integration and processing activities in a variety of scientific disciplines (atmospheric data analysis and weather forecasting, environmental pollution mapping, exposure assessment, etc.). The choice of an adequate assimilation approach is primarily a conceptual modelling affair that is supported by the physical and logical features of the environmental situation under consideration, rather than merely relying on pure inductive schemes and statistical arguments. In many cases, the assimilation approach involves conditionalization techniques which enable it to merge various forms of site-specific data with modelling frameworks. We distinguish between interpretive conditionalization (which refers to the physical and epistemic characteristics of assimilation modelling) and formal conditionalization (which is a purely mathematical construction), and emphasize the importance of an adequate choice of the latter to represent the former. We point out the considerable improvements of the operational Bayesian conditionalization approach over the standard Bayesian rule. Statistical learning techniques are useful at the descriptive/correlation level of scientific inquiry, whereas the level of explanation/prediction is the domain of advanced operational techniques. An operational approach is based on an epistemic view in which the relevant natural processes are represented in terms of spatiotemporal random fields and the probability laws are expressed in terms of physically meaningful operators. The spirit of this view is to keep the field solutions consistent with the site-specific information (hard data, uncertain observations, etc.) while exactly satisfying the constraints arising from general knowledge sources (physical laws, primitive equations, scientific theories, etc.). Natural field probabilities in space and time are derived for non-Bayesian operational conditionals based on deductively sound inference, which are valid for a wide variety of knowledge bases. Analytical and numerical comparisons are made between Bayesian and non-Bayesian conditionals, and insight is gained in terms of examples and applications which cut across various earth science disciplines. Operational Bayesian conditionalization is a powerful and versatile component of assimilation modelling with many applications in environmental sciences, although in certain cases a non-Bayesian conditional (based on deductive logic and the characterization of physical connection) may provide a meaningful description of the data assimilation framework suggested by the laws of nature. (C) 2002 Elsevier Science Ltd. All rights reserved.