论文标题
通过标准化流从显式密度产生有效的采样
Efficient sampling generation from explicit densities via Normalizing Flows
论文作者
论文摘要
对于许多应用，例如计算不同幅度的预期值，从已知概率密度函数（目标密度）取样至关重要，但通过反变形而具有挑战性。在这些情况下，排斥和重要性采样需要合适的提案密度，可以有效地评估和采样。我们将提出一种基于归一化流量的方法，提出了一个解决方案，即由于目标密度在流动转换区域的目标密度为0，因此爆炸反向kullback-leibler差异的常见问题。该方法的性能将使用多模式复杂密度函数来证明。
For many applications, such as computing the expected value of different magnitudes, sampling from a known probability density function, the target density, is crucial but challenging through the inverse transform. In these cases, rejection and importance sampling require suitable proposal densities, which can be evaluated and sampled from efficiently. We will present a method based on normalizing flows, proposing a solution for the common problem of exploding reverse Kullback-Leibler divergence due to the target density having values of 0 in regions of the flow transformation. The performance of the method will be demonstrated using a multi-mode complex density function.
