A random-batch Monte Carlo method for many-body systems with singular kernels

14 Mar 2020 Lei Li Zhenli Xu Yue Zhao

We propose a fast potential splitting Markov Chain Monte Carlo method which costs $O(1)$ time each step for sampling from equilibrium distributions (Gibbs measures) corresponding to particle systems with singular interacting kernels. We decompose the interacting potential into two parts, one is of long range but is smooth, and the other one is of short range but may be singular... (read more)

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