对于受约束和规范估计的贝叶斯推理的近似MCMC.
Xinkai Zhou1, Qiang Heng2, Eric C Chi3
1Department of Biostatistics, UCLA.
The American statistician
|December 27, 2024
概括
接近马尔科夫链蒙特卡洛 (ProxMCMC) 为复杂的估计问题提供了一个灵活的贝叶斯推理框架. 这种增强的方法允许对数据进行适应性参数估计,并使用先进的采样算法对高维数据进行缩放.
科学领域:
- 计算统计学 计算统计学
- 贝叶斯的推理是贝叶斯的推理.
- 机器学习 机器学习
背景情况:
- 近接马尔科夫链蒙特卡洛 (ProxMCMC) 最初是为贝叶斯成像开发的.
- 现有的ProxMCMC方法使用固定的参数和Langevin算法.
- 约束和规范化的估计在频率主义和贝叶斯统计学中都带来了挑战.
研究的目的:
- 将ProxMCMC扩展到一个完全贝叶斯的框架.
- 为了使所有参数的数据适应性估计,包括规范化强度.
- 为了提高高维问题的可扩展性.
主要方法:
- 通过结合数据适应参数估计,开发了一个完全贝叶斯式的ProxMCMC.
- 使用莫罗-约西达包裹,以顺利近似总变化规范化.
- 采用先进的采样算法,如哈密尔顿式蒙特卡洛,以提高可扩展性.
主要成果:
- 在各种统计估计任务中展示了ProxMCMC的多功能性.
- 展示了框架处理以前被认为是难以解决的问题的能力.
- 在ProxMCMC.中验证了数据适应参数估计的有效性.
结论:
- ProxMCMC提供了一个强大而模块化的贝叶斯推理方法.
- 扩展框架解决了以前ProxMCMC实施的局限性.
- ProxMCMC适用于各种具有挑战性的统计和机器学习问题.
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