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首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >Almost instant time inference for hybrid partially dynamic Bayesian networks
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Almost instant time inference for hybrid partially dynamic Bayesian networks

机译：混合部分动态贝叶斯网络的几乎即时时间推断

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摘要

A Bayesian network (BN) is a compact representation for probabilistic models and inference. They have been used successfully for many military and civilian applications. It is well known that, in general, the inference algorithms to compute the exact a posterior probability of a target node given observed evidence are either computationally infeasible for dense networks or impossible for general hybrid networks. In those cases, one either computes the approximate results using stochastic simulation methods or approximates the model using discretization or a Gaussian mixture model before applying an exact inference algorithm. This paper combines the concept of simulation and model approximation to propose an efficient algorithm for those cases. The main contribution here is a unified treatment of arbitrary (nonlinear non-Gaussian) hybrid (discrete and continuous) BN inference having both computation and accuracy scalability. The key idea is to precompile the high-dimensional hybrid distribution using a hypercube representation and apply it for both static and dynamic BN inference. Since the inference process essentially becomes a combination of table look-up and some simple operations, the method is shown to be extremely efficient. It can also he scaled to achieve any desirable accuracy given sufficient preprocessing time and memory for the cases where exact inference is not possible
机译：贝叶斯网络（BN）是概率模型和推论的紧凑表示。它们已成功用于许多军事和民用应用。众所周知，通常，给定观察到的证据来计算目标节点的后验概率的确切推理算法对于密集网络在计算上是不可行的，对于一般的混合网络是不可能的。在这些情况下，在应用精确推理算法之前，要么使用随机仿真方法计算近似结果，要么使用离散化或高斯混合模型近似模型。本文结合了仿真和模型逼近的概念，为这些情况提出了一种有效的算法。这里的主要贡献是对具有计算和精度可伸缩性的任意（非线性非高斯）混合（离散和连续）BN推论进行统一处理。关键思想是使用超立方体表示预编译高维混合分布，并将其应用于静态和动态BN推断。由于推理过程实质上变成了表查找和一些简单操作的组合，因此该方法被证明是非常有效的。在没有足够精确推理的情况下，如果有足够的预处理时间和足够的存储空间，它也可以缩放以达到任何期望的精度

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