A new method is developed for performing sufficient dimension reduction when probabilistic graphical models are being used to estimate parameters. The procedure enriches the domain of application of dimension reduction techniques to settings where (i) p the number of variables in the model is much larger than the available sample size n, (ii) p is much larger than the number of slices H the model uses and (iii) D the number of projection vectors can be larger than the number of slices H. The methodology is developed for the case of the sliced inverse regression model, but extensions to other dimension reduction techniques such as sliced average variance estimation or other methods are straightforward.

当使用概率图形模型来估计参数时，开发了一种新方法来执行充分的降维。该过程丰富了降维技术的应用领域，其中 (i) p模型中的变量数量远大于可用样本大小n，(ii) p远大于切片数量H模型使用和 (iii) D投影向量的数量可以大于切片的数量H. 该方法是为切片逆回归模型的情况而开发的，但对其他降维技术（如切片平均方差估计或其他方法）的扩展很简单。