标题： 统计模型聚合通过参数匹配 显示英文标题

标题： Statistical Model Aggregation via Parameter Matching

摘要： 我们研究了从隔离的、可能异构的数据集中聚合模型的问题。利用贝叶斯非参数工具，我们开发了一种通用的元建模框架，通过识别局部模型参数化之间的对应关系来学习共享的全局潜在结构。我们提出的框架是模型无关的，并且适用于广泛的模型类型。在模拟数据上验证了我们的方法后，我们在聚合高斯主题模型、基于层次狄利克雷过程的隐藏马尔可夫模型以及稀疏高斯过程方面展示了其实用性，其应用范围涵盖了文本摘要、运动捕捉分析和温度预测等领域。 显示英文摘要

摘要： We consider the problem of aggregating models learned from sequestered, possibly heterogeneous datasets. Exploiting tools from Bayesian nonparametrics, we develop a general meta-modeling framework that learns shared global latent structures by identifying correspondences among local model parameterizations. Our proposed framework is model-independent and is applicable to a wide range of model types. After verifying our approach on simulated data, we demonstrate its utility in aggregating Gaussian topic models, hierarchical Dirichlet process based hidden Markov models, and sparse Gaussian processes with applications spanning text summarization, motion capture analysis, and temperature forecasting.