Extraction of Latent Dynamical Structure from Time-Series Spectral Data
Extraction of Latent Dynamical Structure from Time-Series Spectral Data
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DOI：
10.7566/jpsj.85.104003
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发表时间：
2016-10-15
影响因子：
1.7
通讯作者：
Okada, Masato
中科院分区：
文献类型：
--
作者：
Murata, Shin;Nagata, Kenji;Okada, Masato
从时间序列数据中估计潜在动态是一个重要的问题，在广泛的领域。在这项研究中，我们专注于时间序列光谱数据，这是在行星科学，凝聚态科学，和许多其他领域，和他们的潜在动力学。时间序列光谱数据具有多峰结构，每个峰的中心、宽度和幅度反映了对象的性质。在这里，我们提出了一种方法来估计参数的峰值，其潜在的动力学参数，峰值的数量，并通过使用贝叶斯推断模型的阶数。
The estimation of latent dynamics from time-series data is an important problem in a broad range of fields. In this research, we focused on time-series spectral data, which are obtained in planetary science, condensed matter science, and many other fields, and their latent dynamics. Time-series spectral data have a multiple-peak structure and the center, width, and amplitude of each peak reflect the nature of the subject. Here, we propose a method to estimate the parameters of peaks, the parameters of their latent dynamics, the number of peaks, and the order of the model by using Bayesian inference.