Replacing the target μ by a history-adjusted target μ(x/μ)^{-α} in any graph MCMC sampler gives O(1/α) variance reduction at constant per-step cost, and extends to non-reversible chains.
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Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs
Replacing the target μ by a history-adjusted target μ(x/μ)^{-α} in any graph MCMC sampler gives O(1/α) variance reduction at constant per-step cost, and extends to non-reversible chains.