贝叶斯推理的最小复杂的模型与任意顺序的相互作用的贝叶斯推理
Clélia de Mulatier1,2, Matteo Marsili3
1University of Amsterdam, Institute for Theoretical Physics and Informatics Institute, Science Park 904, 1098 XH Amsterdam, the Netherlands.
我们介绍了用于分析高维二进制数据的最小复杂模型 (MCM). 这些模型有效地识别了复杂的,高阶依赖关系,简化了在大数据集中发现模式的过程.
科学领域:
- 统计建模 统计建模
- 机器学习是机器学习.
- 信息理论是信息理论.
背景情况:
- 分析具有复杂,高阶模式的高维数据集具有挑战性.
- 传统方法往往侧重于双向相关性,缺少复杂的变量相互作用.
研究的目的:
- 开发一种计算上可行的方法来识别描述高维二进制数据的最佳统计模型.
- 通过使用一个新的模型家族,探索超越对对相关的高阶依赖关系.
主要方法:
- 引入最小复杂模型 (MCMs):最大模型与分组的高阶相互作用.
- 开发MCM高效的贝叶斯模型选择,使模型空间的快速探索.
- 用于表示独立的统计建模的尺度转换不变性.
主要成果:
- 对MCM的贝叶斯模型选择在计算上是高效的,不需要参数的拟合.
- 该MCM框架允许快速推断和采样.
- MCM有效地揭示了高阶依赖关系,并产生可伪造的预测.
结论:
- 最少复杂的模型提供了一种强大而有效的方法来建模高维二进制数据中的复杂依赖关系.
- 这种方法简化了复杂模式的发现,并提供了对系统对称性和不变性的洞察.
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