中文说明： 高斯过程回归和分类程序。精彩的工作方案。 简单的一维回归运动计算中两种不同方式： 一例无噪声与立方相关模型 一例嘈杂平方欧几里得相关模型 在这两种情况下，模型参数估计使用的最大似然原理。 数字说明高斯过程模型，以及其形式的点态的 95%置信区间的概率性质的插值属性。 请注意参数金块应用作为正则化方法的培训点之间的假设的协方差。在平方欧几里得相关模型的特殊情况下，熔核是数学上等价于一个归一化方差： 这就是
English Description：
The program for gaussian processs regression and classification. Wonderful working programme. A simple one-dimensional regression exercise computed in two different ways: A noise-free case with a cubic correlation model A noisy case with a squared Euclidean correlation model In both cases, the model parameters are estimated using the maximum likelihood principle. The figures illustrate the interpolating property of the Gaussian Process model as well as its probabilistic nature in the form of a pointwise 95% confidence interval. Note that the parameter nugget is applied as a Tikhonov regularization of the assumed covariance between the training points. In the special case of the squared euclidean correlation model, nugget is mathematically equivalent to a