Efficient sampling generation from explicit densities via Normalizing Flows
This work addresses a specific problem in computational statistics for applications requiring sampling from explicit densities, but it appears incremental as it builds on normalizing flows to handle known bottlenecks.
The paper tackles the challenge of efficiently sampling from known probability densities, particularly when target densities have zero values causing issues with reverse KL divergence, and demonstrates the method's performance on a multi-mode complex density function.
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.