贝叶斯数据素描用于变系数回归模型的贝叶斯数据素描
Rajarshi Guhaniyogi1, Laura Baracaldo2, Sudipto Banerjee3
1Department of Statistics Texas A & M University College Station, TX 77843-3143, USA.
概括
贝叶斯数据素描可以加快对大型功能数据的分析. 这种方法压缩了数据,以便在没有新的算法或硬件的情况下进行高效的变系数模型推断.
科学领域:
- 统计 统计 统计 统计
- 机器学习 机器学习
- 功能数据分析 功能数据分析
背景情况:
- 变系数模型对于功能数据分析中的非线性回归至关重要.
- 对这些模型的贝叶斯式方法对于大型数据集来说是计算密集的,阻碍了它们的应用.
- 马尔科夫链蒙特卡洛 (MCMC) 算法有助于减缓后置计算.
研究的目的:
- 引入贝叶斯数据素描作为一个高效的计算方法,用于变系数模型的大样本大小.
- 为了在不需要新的模型,算法或专门的硬件的情况下,在功能数据上实现更快的贝叶斯推理.
- 在压缩数据上证明已建立的可变系数回归方法的适用性.
主要方法:
- 使用随机线性转换进行维度缩小的数据压缩.
- 在压缩的功能响应向量和预测矩阵上进行贝叶斯推理.
- 将已确定的变系回归算法应用于缩小维度数据.
主要成果:
- 建立后部收缩率用于估计不同的系数和预测用压缩数据的结果.
- 通过模拟实验证明了推断和计算效率.
- 验证了远程传感植被数据的方法,展示了实际的实用性.
结论:
- 贝叶斯数据素描为大规模的功能数据分析提供了一个计算效率高的解决方案.
- 该方法保留了基于模型的贝叶斯推理的完整性,同时显著降低了计算负担.
- 这种技术有助于在大数据应用中更广泛地采用贝叶斯波动系数模型.
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