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使用变量函数混合模型进行超快的近似推理

Shuning Huo¹, Jeffrey S Morris², Hongxiao Zhu¹

¹Department of Statistics, Virginia Tech.

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America

|August 23, 2023

概括

我们开发了一个快速的计算框架来分析高维的功能数据. 这种方法使用变量贝叶斯来进行超快速的近似推断,克服传统贝叶斯函数混合模型的局限性.

科学领域:

计算统计学计算统计学
功能数据分析功能数据分析
高维数据分析高维数据分析

背景情况:

贝叶斯函数混合模型对复杂的函数数据有效.
后端采样中的计算挑战限制了它们对高维数据的应用.

研究的目的:

引入一种新的计算框架,用于在高维函数数据中进行超快速近似推断.
解决现有的贝叶斯函数混合模型的计算局限性.

主要方法:

用于功能观测的节的基础表示.
采用变量贝叶斯来近似后面分布,避免了计算密集的马尔科夫链蒙特卡洛 (MCMC) 采样.
实现了一个用于参数估计的快速代算法,以及在基础空间中的快速多重测试程序.

主要成果:

拟议的框架可以实现高效的压缩和并行计算.
展示了超快速的近似推断能力.
成功识别显著的本地区域,表明模拟研究和现实世界数据集 (蛋白质组学,脑部成像) 中的群体差异.

结论:

新的框架为高维函数数据分析提供了计算效率高的解决方案.
它为传统方法提供了可行的替代方案,使得推断更快,更具可扩展性.
该方法通过模拟和各种科学领域的应用来验证.

关键词:

估计贝叶斯推理的推理分布式推理分布式推理功能数据分析功能数据分析并行计算是一种平行计算.变量贝叶斯是变量的贝叶斯.

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