斯过程模型之间的区别:主动学习和静态结构
Elham Yousefi1, Luc Pronzato2, Markus Hainy1
1Institute of Applied Statistics, Johannes Kepler University, Altenberger Straße 69, 4040 Linz, Austria.
Statistical papers (Berlin, Germany)
|August 31, 2023
概括
这项研究引入了新的实验设计,用于区分高斯过程模型. 它评估了顺序和静态标准,以优化机器学习和计算机实验中的模型歧视.
科学领域:
- 统计 统计 统计 统计
- 机器学习 机器学习
- 实验设计 实验设计
背景情况:
- 具有不同协差内核的高斯过程模型是计算机实验,战争,传感器放置和机器学习的基础.
- 区分这些模型对于准确的预测和可靠的分析至关重要.
研究的目的:
- 开发和分析实验设计,以有效地区分两个高斯过程模型.
- 为了比较模型选择的顺序和静态设计标准.
主要方法:
- 研究了基于最大化Kullback-Leibler分歧或最小化平均平方误差的顺序设计策略.
- 检查了静态标准,包括日志概率比率和弗雷切距离.
- 引入了基于距离的新,计算上更简单的标准,并为近似设计推导了最佳性条件.
主要成果:
- 建立了各种歧视标准之间的数学关系.
- 提供了数值示例,展示了拟议方法的性能.
- 在近似设计设置中确定了最佳设计措施的必要条件.
结论:
- 该研究提供了一个全面的框架,用于设计实验,以区分高斯过程模型.
- 提出的方法和标准提高了在各种科学和工程应用中模型选择的效率和准确性.
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