Mathematical Sciences: Statistical Theory for Classification

数学科学：分类统计理论

• 批准号: 9401296
• 负责人: John Hartigan
• 金额: $ 20.4万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1994
• 资助国家: 美国
• 起止时间: 1994-07-01 至 1997-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9401296&HistoricalAwards=false
• 关键词: Mathematical; Sciences; Statistical; Theory; Classification; Mathematical; Sciences; Statistical; Theory; Classification

The proposal develops classification based approaches to prediction and statistical inference. Examples of statistical problems considered are change point problems, histograms, mode identification, reticulate evolution, and quantization of probability ditributions. A particular classification problem studied is the problem of types, the family of classes that most succinctly describes a primitive given family of classes. One way of predicting the future is by recognising circumstances that are similar to circumstances already experienced, that is, by placing the present circumstances in some established class of circumstances, from which we can infer other aspects of th future. Our method of classifying experience will determine how we predict the future. Thus we need to study classification to study prediction. We can't simply seize on some scheme of classification and use it permanently; we must develop new classifications adapting to the success or failure of predictions. Thus classification is needed for practical probability judgments, and practical probability judgments are needed for classification.

该提案开发了基于分类的预测和统计推断的方法。所考虑的统计问题的示例是变化点问题，直方图，模式识别，网状演化和概率含量的量化。研究的一个特定的分类问题是类型的问题，类型是最简洁地描述给定阶级家族的阶级家族。 预测未来的一种方法是认识到与已经经历的环境相似的情况，也就是说，将当前情况置于某些既定的情况类别，我们可以从中推断出未来的其他方面。我们分类经验的方法将决定我们如何预测未来。因此，我们需要研究分类以研究预测。我们不能简单地抓住某种分类方案并永久使用它；我们必须开发新的分类，以适应预测的成功或失败。因此，需要进行分类来进行实际概率判断，并且需要进行分类的实际概率判断。