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过程混合的高斯过程模型混合采样推理
引用本文:雷菊阳,黄克,许海翔,史习智. 过程混合的高斯过程模型混合采样推理[J]. 上海交通大学学报, 2010, 44(2): 271-0275
作者姓名:雷菊阳  黄克  许海翔  史习智
作者单位:(1.上海交通大学 机械系统与振动国家重点实验室, 上海 200240;2.上海工程技术大学 机械工程学院, 上海 201620; 3.上海特检院,上海 200062)
基金项目:上海市科委基础研究资助项目(05JC14026)
摘    要:提出了基于Dirichlet过程混合的高斯过程模型揭示复杂动态系统结构数据的多态性的内在机制.针对均值结构与协方差结构稀疏性的差异性,设计了参数先验与非参数先验来构建基于Polya urn与过松弛层采样的混合采样框架体系.该混合采样方案不但能够在统一的Metropolis Hasting(M H)概率评价准则下实现,而且能够最大限度地克服高斯随机走步的缺陷,方便、快速地获得马尔科夫样本链的展开.仿真结果表明,混合采样算法比高斯过程回归模型及高斯过程函数回归混合模型具有更广泛的适应性及更好的预测效果.

关 键 词:混合采样   非参数贝叶斯推理   Dirichlet过程混合   高斯过程  
收稿时间:2009-04-02

Study on Hybrid Sampling Inference for Dirichlet Process Mixture of Gaussian Process Model
LEI Ju-yang,HUANG Ke,XU Hai-xiang,SHI Xi-zhi. Study on Hybrid Sampling Inference for Dirichlet Process Mixture of Gaussian Process Model[J]. Journal of Shanghai Jiaotong University, 2010, 44(2): 271-0275
Authors:LEI Ju-yang  HUANG Ke  XU Hai-xiang  SHI Xi-zhi
Affiliation:(1.State Key Laboratory of Mechanical System & Vibration, Shanghai Jiaotong University,Shanghai 200240, China; 2.College Mechanical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China; 3.Special Equipment Inspection Institute, Shanghai 200062, China)
Abstract:Dirichlet process mixture of Gaussian process model was proposed to reveal the intrinsic mechanism of multi-model of complex dynamic system architecture data.As for the difference between the mean structure and covariance structure of sparsity,parametric a priori and non-parametric a priori were designed based on the hybrid sampling framework of Polya urn sampling and over-relaxed sliced sampling.The hybrid sampling will not only be implemented under the unified Metropolis-Hasting probability evaluation cri...
Keywords:hybrid sampling  Bayesian nonparametric inference  Dirichlet process mixture  Gaussian process
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