• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International journal of management science and engineering management >Seeking relationships in big data: a Bayesian perspective
【24h】

Seeking relationships in big data: a Bayesian perspective

机译：在大数据中寻求关系：贝叶斯观点

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

The real purpose of collecting big data is to identify causality in the hope that this will facilitate credible predictivity. But the search for causality can trap one into infinite regress, and thus one takes refuge in seeking associations between variables in data sets. Regrettably, the mere knowledge of associations does not enable predictivity. Associations need to be embedded within the framework of the probability calculus to make coherent predictions. This is so because associations are a feature of probability models, and hence they do not exist outside the framework of a model. Measures of association, like correlation, regression, and mutual information merely refute a preconceived model. Estimated measures of associations do not lead to a probability model; a model is the product of pure thought. This paper discusses these and other fundamentals that are germane to seeking associations in particular, and machine learning in general.
机译：收集大数据的真正目的是确定因果关系，希望这将有助于可靠的预测。但是，因果关系的搜索可以使人陷入无限回归，因此人们在寻求数据集中变量之间的关联时会有所顾忌。遗憾的是，仅仅具有关联性的知识无法实现可预测性。关联需要嵌入概率演算的框架内，以进行连贯的预测。之所以如此，是因为关联是概率模型的特征，因此关联不存在于模型框架之外。关联的度量（例如相关性，回归和互信息）仅反驳了先入为主的模型。关联的估计量度不会导致概率模型；模型是纯思想的产物。本文讨论了这些和其他基础知识，这些基础知识与寻求关联特别是与机器学习有关。

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