将贝叶斯模型的链条与马尔科夫融合相结合.
Andrew A Manderson1, Robert J B Goudie2
1MRC Biostatistics Unit, University of Cambridge, United Kingdom, and The Alan Turing Institute.
概括
链式马尔科夫合并提供了一种新的方法,通过将不同的子模型连接起来,在贝叶斯推理中结合多个数据源. 这种方法有助于管理先前的依赖关系,并有效地估计复杂的联合模型.
科学领域:
- 统计 统计 统计 统计
- 计算生物学 计算生物学
- 生物统计学 生物统计学
背景情况:
- 贝叶斯推理通常需要整合多个异质数据集.
- 为各种数据指定一个单一的模型可能很复杂.
- 每个数据源的不同子模型提供了一个灵活的替代方案.
研究的目的:
- 引入链式马尔科夫接 (CMM),用于组合子模型的链.
- 为了应对先前依赖和相邻子模型之间的和解所面临的挑战.
- 为集成模型开发一种高效的后置估计方法.
主要方法:
- CMM将马尔科夫融合扩展到由共同数量连接的子模型链.
- 该方法捕捉了子模型内部的先前依赖性,并协调了子模型之间的先前差异.
- 一个多阶段的,潜在的平行采样器被描述为后期估计.
主要成果:
- 通过生态综合人口模型展示了CMM.
- 将CMM应用于具有不确定的事件时间的联合纵向和时间到事件模型.
- 该方法有效地整合了来自多个子模型的信息.
结论:
- 链式马尔科夫融合提供了一个概念上有吸引力的框架,可以通过子模型集成各种数据.
- 该方法在复杂的生态和生物医学环境中促进了强大的贝叶斯推理.
- 通过利用子模型结构,CMM可以有效估计联合模型.
相关概念视频
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