Mathematical Sciences: Statistical Theory for Classification

该提案开发了基于分类的预测和统计推断方法。统计问题的例子被认为是变点问题，直方图，模式识别，网状演变，和量化的概率distributions。一个特殊的分类问题研究的是问题的类型，家庭的类，最简洁地描述了一个原始的家庭的类。 预测未来的一种方法是通过识别与已经经历过的情况相似的情况，也就是说，通过将目前的情况置于某种既定的情况中，我们可以从中推断未来的其他方面。我们对经验的分类方法将决定我们如何预测未来。因此，我们需要研究分类来研究预测。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方法，以适应预测的成功或失败。因此，实际概率判断需要分类，分类需要实际概率判断。

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.