提出分支版SVGD，更好采样多峰分布。
Branching Stein Variational Gradient Descent for sampling multimodal distributions
- 引入随机分支机制，增强状态空间探索能力。
- 理论证明分布收敛性，实验显示采样质量更优。
- 适合多峰分布采样，尤其在复杂后验中表现佳。
我们提出一种新型基于粒子的变分推断方法，用于处理多峰分布。该方法称为分支斯坦因变分梯度下降（BSVGD），通过引入随机分支机制扩展了经典斯坦因变分梯度下降（SVGD）算法，以促进状态空间的探索。本文给出了分布收敛性的理论保证，并通过数值实验验证了算法的有效性。通过样本间的沃尔什距离及计算时间，对BSVGD与SVGD进行了性能比较。
原文摘要 · Abstract (English)
We propose a novel particle-based variational inference method designed to work with multimodal distributions. Our approach, referred to as Branched Stein Variational Gradient Descent (BSVGD), extends the classical Stein Variational Gradient Descent (SVGD) algorithm by incorporating a random branching mechanism that encourages the exploration of the state space. In this work, a theoretical guarantee for the convergence in distribution is presented, as well as numerical experiments to validate the suitability of our algorithm. Performance comparisons between the BSVGD and the SVGD are presented using the Wasserstein distance between samples and the corresponding computational times.
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