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首页> 外文期刊>Procedia Computer Science >Architecting Very Large, Complex Bayesian Network Simulations for Practical Airworthiness Risk Assessment Applications
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Architecting Very Large, Complex Bayesian Network Simulations for Practical Airworthiness Risk Assessment Applications

机译：为实际的适航风险评估应用程序设计大型，复杂的贝叶斯网络仿真

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While constructing practical Bayesian Networks (BN) of large, complex systems that are computationally tractable continues to be a significant challenge within the expanding BN modeling and simulation field, this paper advances methods that are changing the BN modeling paradigm from simplifying these models to reducing them by employing parent divorcing and generalized BN model development methods. We illustrate real world implementation of these concepts by reducing a large, complex cyber-physical risk assessment BN model into smaller, manageable ones able to be efficiently simulated, while identifying a newly structured approach toward appropriately weighting the integrating factors to be validated at the higher level. We apply d-connectedness techniques to maintain backpropagation and overall model integrity present in the larger, complex BN model, and use cause consequence and definitional/synthesis idioms to construct an Airworthiness BN risk assessment model, based on MIL-HDBK-516C, by implementing series and parallel networks of smaller models that simulate separately. Richer simulation results are enabled by extending the degree of model complexity able to be created and efficiently simulated within the practical capabilities of an existing commercial tool.
机译：在不断扩展的BN建模和仿真领域中，构建大型的复杂系统且易于计算的实用贝叶斯网络（BN）仍然是一项重大挑战，但本文提出了从简化这些模型到简化BN建模范式的方法通过采用父母离婚和广义的BN模型开发方法。我们通过将大型，复杂的网络物理风险评估BN模型简化为可以有效模拟的较小，可管理的模型，同时确定了一种新结构的方法，以适当地权衡待在较高层进行验证的整合因子，从而说明了这些概念在现实世界中的实现水平。我们使用d-connectedness技术来维持较大复杂BN模型中存在的反向传播和整体模型完整性，并通过执行分别模拟的较小模型的串联和并联网络。通过扩展能够在现有商业工具的实际功能范围内创建和有效仿真的模型复杂程度，可以实现更丰富的仿真结果。

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