• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Statistics and computing >Hierarchical Gaussian process mixtures for regression
【24h】

Hierarchical Gaussian process mixtures for regression

机译：分层高斯过程混合进行回归

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

As a result of their good performance in practice and their desirable analytical properties, Gaussian process regression models are becoming increasingly of interest in statistics, engineering and other fields. However, two major problems arise when the model is applied to a large data-set with repeated measurements. One stems from the systematic heterogeneity among the different replications, and the other is the requirement to invert a covariance matrix which is involved in the implementation of the model. The dimension of this matrix equals the sample size of the training data-set. In this paper, a Gaussian process mixture model for regression is proposed for dealing with the above two problems, and a hybrid Markov chain Monte Carlo (MCMC) algorithm is used for its implementation. Application to a real data-set is reported.
机译：由于其在实践中的良好性能和理想的分析特性，高斯过程回归模型在统计，工程和其他领域变得越来越受关注。但是，当将模型应用于具有重复测量的大型数据集时，会出现两个主要问题。一个源于不同复制品之间的系统异质性，另一个是要求反转协方差矩阵的需求，该协方差矩阵涉及模型的实现。该矩阵的维数等于训练数据集的样本大小。针对上述两个问题，提出了一种高斯混合过程回归模型，并采用了混合马尔可夫链蒙特卡罗算法。报告了对实际数据集的应用。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号