MCMC algorithm is well-known for having difficulty exploring distant modes when the target distribution is multi-modal. The reason is that a proposal state is likely to be rejected when traversing across low density regions. Focusing on this issue, we proposed parallel generalized elliptical slice sampling algorithm with adaptive regional pseudo-prior (RGESS). Different from the work of Fagan et al. [2016. Elliptical slice sampling with expectation propagation. In: UAI] and Nishihara et al. [Parallel mcmc with generalized elliptical slice sampling. J Mach Learn Res. 2014;15:2087–2112], different pseudo-priors are used at different regions to conduct the generalized elliptical slice sampling (GESS) algorithm. The rejection rate is modified to guarantee detailed balance condition. We also employ adaptive transition kernel and parallel computing to accelerate sampling speed. Experimental results on one synthetic and one real-world dataset show that the proposed algorithm has the following advantages: with the same starting points, the proposed algorithm spend less time to find the modes of the target distribution; after finding the modes, the proposed algorithm has less interactions between modes due to parameters adaption, which leads to lower rejection rate; when estimating the parameters of multi-modal posterior distributions, the samples generated by proposed algorithm can find different modes better.

中文翻译：

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具有自适应区域伪先验的并行广义椭圆切片采样

当目标分布是多模态时，MCMC 算法难以探索远距离模式是众所周知的。原因是提议状态在穿越低密度区域时可能会被拒绝。针对这个问题，我们提出了自适应区域伪先验（RGESS）的并行广义椭圆切片采样算法。与 Fagan 等人的工作不同。[2016. 具有期望传播的椭圆切片采样。在：UAI] 和 Nishihara 等人。[具有广义椭圆切片采样的并行 mcmc。J Mach 学习资源。2014;15:2087-2112]，在不同的区域使用不同的伪先验来进行广义椭圆切片采样（GESS）算法。修改废品率以保证详细的平衡条件。我们还采用自适应转换内核和并行计算来加快采样速度。在一个合成数据集和一个真实世界数据集上的实验结果表明，该算法具有以下优点：在起点相同的情况下，该算法寻找目标分布模式的时间更少；找到模式后，该算法由于参数自适应，模式之间的交互较少，从而导致较低的拒绝率；在估计多模态后验分布的参数时，该算法生成的样本可以更好地找到不同的模态。所提出的算法花费更少的时间来寻找目标分布的模式；找到模式后，该算法由于参数自适应，模式之间的交互较少，从而导致较低的拒绝率；在估计多模态后验分布的参数时，该算法生成的样本可以更好地找到不同的模态。所提出的算法花费更少的时间来寻找目标分布的模式；找到模式后，该算法由于参数自适应，模式之间的交互较少，从而导致较低的拒绝率；在估计多模态后验分布的参数时，该算法生成的样本可以更好地找到不同的模态。