根据粒子位置测量预测粒子类型：物理学家对贝叶斯分类的方法
Prediction of particle type from measurements of particle location: A physicist's approach to Bayesian classification
摘要：
贝叶斯方法预测的颗粒类型的颗粒位置的测量进行了探讨，使用参数模型，其先验是基于变换组。两种类型的粒子被认为是，和位置表示在一个单一的空间坐标。几种情况下，对应于不同状态的先验知识进行评估，包括测量不确定性的影响。与最近邻分类和核密度估计进行了比较。如何可以评估的可靠性预测仅从现有的数据进行了讨论。
英文摘要：
The Bayesian approach to the prediction of particle type given measurements of particle location is explored, using a parametric model whose prior is based on the transformation group. Two types of particle are considered, and locations are expressed in terms of a single spatial coordinate. Several cases corresponding to different states of prior knowledge are evaluated, including the effect of measurement uncertainty. Comparisons are made to nearest neighbor classification and kernel density estimation. How one can evaluate the reliability of the prediction solely from the available data is discussed.
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