提出数学语言框架，让机器能自动从实验中提炼知识。
On Experiments
- 用数学语言形式化科学实验流程
- 给出计算信息缺失量的线性规划方法
- 改进经典定理证明，适合理论研究者
科学过程是将实验结果转化为对世界认知的途径。大量研究致力于自动化这一过程，为此需用精确的数学语言描述科学方法。本文提出一种这样的语言体系。内容虽非全新，但整合了古今重要思想。本文新贡献包括：提出一种新的通用数据处理不等式；对标准损失函数实现偏差-方差分解；简化黑威尔-谢尔曼-斯坦及随机化定理的证明；提供通过线性规划计算信息缺陷的方法。
原文摘要 · Abstract (English)
The scientific process is a means to turn the results of experiments into knowledge about the world in which we live. Much research effort has been directed toward automating this process. To do this, one needs to formulate the scientific process in a precise mathematical language. This paper outlines one such language. What is presented here is hardly new. The material is based on great thinkers from times past well as more modern contributions. The novel contributions of this paper are: A new general data processing inequality, a bias variance decomposition for canonical losses, streamlined proofs of the Blackwell-Sherman-Stein and Randomization theorems. means of calculating deficiency through linear programming.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。