In this contribution, we develop and present a Bayesian probabilistic framework for the representation of complex systems and apply this to an industrial case of offshore environmental load modeling. Based on previous contributions on probabilistic modeling using Bayesian networks, we consider the case where both the model structure and its parameters are estimated from data. Gaussian process-based discrepancy modeling is introduced to represent uncertainties associated with data, when data are produced by models themselves. Two approaches are then introduced on how to deal with multiple model candidates, that is, Bayesian model averaging and decision context-specific model selection. The latter comprising the main novelty of this paper. Two examples are provided: (i) a principal example illustrating the simple but fundamental idea of context-specific model building and (ii) an industrial-scale example considering optimal ranking of evacuation options for platform personnel in the event of an emerging storm.

中文翻译：

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复杂系统的贝叶斯概率表示：应用于波浪载荷建模

在这篇文章中，我们开发并提出了一个用于表示复杂系统的贝叶斯概率框架，并将其应用于海上环境负荷建模的工业案例。基于先前对使用贝叶斯网络进行概率建模的贡献，我们考虑了从数据中估计模型结构及其参数的情况。当数据由模型本身产生时，引入了基于高斯过程的差异建模来表示与数据相关的不确定性。然后介绍了如何处理多个候选模型的两种方法，即贝叶斯模型平均和特定于决策上下文的模型选择。后者构成了本文的主要新颖性。提供了两个示例：