The quantization of a (probability) measure is replacing it by a sum of Dirac masses that is close enough to it (in some metric space of probability measures). Various methods exists to do so, but the situation of quantizing a conditional law has been less explored. We propose a method, called DCMQ, involving a Huber-energy kernel-based approach coupled with a deep neural network architecture. The method is tested on several examples and obtains promising results.

翻译：一种(概率)措施的量化正在以与其相当接近的一组Dirac质量来取代它(在某些概率衡量的衡量尺度上)。有各种方法可以这样做,但是对一项有条件法律的量化情况探索较少。我们提出了一种方法,称为DCMQ,涉及一种基于休伯能源内核的方法,加上一种深厚的神经网络结构。这种方法在几个例子中进行了测试,并取得了有希望的结果。