一个准确的概率步骤查询器用于时间序列分析
Alex Rojewski1,2, Maxwell Schweiger1,2, Ioannis Sgouralis3
1Department of Physics, Arizona State University, Tempe, Arizona.
bioRxiv : the preprint server for biology
|October 3, 2023
概括
我们开发了贝叶斯非参数步骤 (BNP-Step),这是分析杂时间序列数据的新工具. 在没有假设动力模型的情况下,BNP-Step准确地找到过渡,改进了隐藏的马尔科夫模型和现有的步骤查找算法.
科学领域:
- 生物物理学的生物物理.
- 统计力学 统计力学
- 数据分析 数据分析
背景情况:
- 噪音时间序列数据在生物物理实验中很常见,例如FRET和力谱学.
- 隐藏的马尔科夫模型 (HMM) 和步骤查找算法是检测过渡的标准,但有局限性.
- HMMs假设指数式持久时间,在稀疏,杂的数据中偏向步骤检测.
- 现有的步骤查找算法使用临时指标和贪的方法,缺乏稳定性.
结论:
- BNP-Step提供了一种更准确,更强大的方法来分析杂时间序列数据中的离散过渡.
- 在合成和实力光谱数据上表现出卓越的性能.
- 提供了一个强大的新工具,用于生物物理数据分析,精确的过渡检测至关重要.
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