错误规范 - - 在高维度中强大的无概率推断
Owen Thomas1, Raquel Sá-Leão2, Hermínia de Lencastre3,4
1Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway.
概括
这项研究引入了一种新的方法,用于在复杂的统计模型中进行无概率推理. 该方法增强了对高维参数空间的计算可扩展性,使挑战性问题的有效分析成为可能.
科学领域:
- 统计推理 统计推理
- 计算统计学 计算统计学
- 机器学习 机器学习
背景情况:
- 在基于模拟器的模型中,无概率推理至关重要.
- 大致贝叶斯计算 (ABC) 难以处理高维参数.
- 现有的方法在复杂的模型中面临可扩展性挑战.
研究的目的:
- 开发一种先进的方法,用于在高维参数空间中进行无概率推理.
- 提高贝叶斯优化方法的效率和可扩展性.
- 为了使强大的后部表征,即使在模型的错误规范.
主要方法:
- 贝叶斯优化扩展到概率近似差异函数的延伸.
- 使用单独的获取函数和参数子集的总结统计.
- 采用一个附加的收购结构与指数化的损失概率.
主要成果:
- 为更高维的参数空间实现了计算可扩展性.
- 在中等尺寸的参数空间中证明了高效的推理.
- 在比较分析中表现优于现有的模块化ABC方法.
- 在一个30维参数空间中成功安装了细菌传播模型.
结论:
- 拟议的方法为无概率推理提供了一个计算效率高且可扩展的解决方案.
- 它为复杂模型提供了强大的后部表征.
- 这种方法具有实际应用,如细菌传播模型分析所示.
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