高信息量多参数模型跨维贝叶斯地球声反演中的平行回火。

IF 1.2 Q3 ACOUSTICS
Stan E Dosso
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引用次数: 0

摘要

跨维贝叶斯反演是一种结合定量模型选择和不确定性估计的从海洋声学数据中估计海底地球声学模型的有效方法。Trans-D反演对海床层数和每一层的地球声学参数进行概率采样,在采样中增加和去除层数,改变了模型的维度。然而,对于涉及高信息量数据或每层大量参数的问题,接受维度更改的概率可能接近于零。这封信考察了平行回火的使用,它采用了一系列相互作用的马尔可夫链,依次放松的可能性,以解决这些具有挑战性的情况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Parallel tempering in trans-dimensional Bayesian geoacoustic inversion for high-information-content data and multi-parameter models.

Trans-dimensional (trans-D) Bayesian inversion is a powerful approach to estimate seabed geoacoustic models from ocean-acoustic data, combining quantitative model selection and uncertainty estimation. Trans-D inversion samples probabilistically over the number of seabed layers and the geoacoustic parameters for each layer, with layers added and removed in sampling, changing the dimension of the model. However, the probability of accepting dimension changes can approach zero for problems involving highly informative data or large numbers of parameters per layer. This Letter examines the use of parallel tempering, which employs a sequence of interacting Markov chains with successively relaxed likelihoods, to address these challenging cases.

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