Efficient sampling generation from explicit densities via Normalizing Flows

• URL: http://arxiv.org/abs/2003.10200v1
• Date: Mon, 23 Mar 2020 12:03:18 GMT
• Title: Efficient sampling generation from explicit densities via Normalizing Flows
• Authors: Sebastian Pina-Otey and Thorsten Lux and Federico S\'anchez and Vicens Gaitan
• Abstract summary: We will present a method based on normalizing flows, proposing a solution for the common problem of exploding reverse Kullback-Leibler divergence. The performance of the method will be demonstrated using a multi-mode complex density function.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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