A new class of Markov random fields enabling lightweight sampling
Jean-Baptiste Courbot and Hugo Gangloff and Bruno Colicchio
这项工作解决了马尔可夫随机字段(MRF)的高效采样问题。 Potts或Ising MRF的采样通常基于Gibbs采样,因此计算成本很高。 我们在这项工作中考虑如何通过与高斯马尔可夫随机字段的链接来规避这个瓶颈。 后者可以通过几种具有成本效益的方式进行采样,我们引入了从实值GMRF到离散值MRF的映射。 由此产生的新类别MRF受益于验证新模型的一些理论属性。 数值结果显示了计算效率方面的大幅性能增益,因为我们的采样速度比Gibbs采样至少快35倍,使用至少37倍的能量,同时表现出接近经典MRF的经验特性。
This work addresses the problem of efficient sampling of Markov random fields (MRF). The sampling of Potts or Ising MRF is most often based on Gibbs sampling, and is thus computationally expensive. We consider in this work how to circumvent this bottleneck through a link with Gaussian Markov Random fields. The latter can be sampled in several cost-effective ways, and we introduce a mapping from real-valued GMRF to discrete-valued MRF. The resulting new class of MRF benefits from a few theoretica...