在拉曼和CARS光谱镜中训练贝叶斯神经网络的Log-Gaussian马过程
Teemu Härkönen1, Erik M Vartiainen1, Lasse Lensu1
1Department of Computational Engineering, School of Engineering Sciences, LUT University, Yliopistonkatu 34, FI-53850, Lappeenranta, Finland. teemu.harkonen@lut.fi.
Physical chemistry chemical physics : PCCP
|January 11, 2024
概括
我们开发了一种使用马分布变量和日志高斯模型创建合成光谱数据用于训练神经网络的新方法,克服了稀缺的现实世界观测的局限性.
科学领域:
- 频谱学是一种光谱学.
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 有限的现实世界的光谱数据阻碍了神经网络的训练.
- 拉曼和连贯的反斯托克斯拉曼散射 (CARS) 光谱产生复杂的数据集.
研究的目的:
- 为神经网络培训生成合成拉曼和CARS光谱数据集.
- 开发一种可靠的方法来估计光谱参数和相关的不确定性.
主要方法:
- 利用马分布的随机变量和逻辑高斯模型来生成合成数据.
- 采用马尔科夫链蒙特卡洛 (MCMC) 来进行参数估计和贝叶斯后置分布.
- 使用高斯过程建模后台函数.
- 训练贝叶斯神经网络 (BNNs) 来估计马过程参数.
主要成果:
- 成功生成了合成拉曼和CARS光谱.
- BNN准确估计了潜在的光谱特征,并提供了不确定性量化.
- 结果与实验性色素和生物化学样本的确定性估计相一致.
结论:
- 提出的方法有效地为BNN生成合成光谱数据.
- 这种方法通过提供不确定性估计来增强光谱分析.
- 适用于有限数据的各种光谱应用.
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