Abstract：
Extracting spin system parameters from 1D high resolution 1H-NMR spectra can be an intricate task requiring sophisticate methods. With a few exceptions methods to perform such a total line shape analysis commonly rely on local optimization techniques which for increasing complexity of the underlying spin system tend to reveal local solutions. In this work we propose a full Bayesian modeling approach based on a quantum mechanical model of the spin system. The Bayesian formalism provides a global optimization strategy which allows to efficiently include prior knowledge about the spin system or to incorporate additional constraints concerning the parameters of interest. The proposed algorithm has been tested on synthetic and real 1D 1H-NMR data for various spin systems with increasing complexity. The results show that the Bayesian algorithm provides accurate estimates even for complex spectra with many overlapping regions, and that it can cope with symmetry induced local minima. By providing an unbiased estimate of the model evidence the proposed algorithm furthermore offers a way to discriminate between different spin system candidates.
摘要：
从1D高分辨率1H-NMR光谱中提取自旋系统参数可能是一项复杂的任务，需要复杂的方法。除了少数例外，执行这种整体线形分析的方法通常依赖于局部优化技术，该技术增加了底层自旋系统的复杂性，从而揭示了局部解决方案。在这项工作中，我们提出了一种基于自旋系统量子力学模型的完整贝叶斯建模方法。贝叶斯形式主义提供了全局优化策略，该策略允许有效地包括有关自旋系统的先验知识或结合有关感兴趣参数的附加约束。所提出的算法已在各种自旋系统的合成和真实1D1H-NMR数据上进行了测试，复杂性越来越高。结果表明，即使对于具有许多重叠区域的复杂光谱，贝叶斯算法也可以提供准确的估计。并且它可以应对对称性引起的局部最小值。通过提供模型证据的无偏估计，所提出的算法还提供了一种区分不同自旋系统候选的方法。
