Quantization of a probability measure means representing it with a finite set of Dirac masses that approximates the input distribution well enough (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：概率测度的量化指的是用一组有限的狄拉克质量来表示输入分布，使其在某种概率测度度量空间中足够好地逼近该分布。虽然已有多种方法实现这一目标，但关于条件分布量化的研究尚不充分。我们提出了一种名为DCMQ的方法，该方法融合了基于Huber能量核的方法与深度神经网络架构。该方法在多个实例上进行了测试，并取得了有前景的结果。