用ChEES-HMC改进SMC采样，速度更快且更高效。
Incorporating the ChEES Criterion into Sequential Monte Carlo Samplers
- 将ChEES-HMC作为SMC的提议分布，提升采样效率。
- 在多个任务上比NUTS更快，且保持相近或更优的采样质量。
- 适合需要快速并行采样的非参数贝叶斯推断场景。
马尔可夫链蒙特卡洛（MCMC）方法虽强大但计算成本高。利用梯度信息的提议方法如哈密顿蒙特卡洛（HMC）若超参数选择得当，能更好探索参数空间。无来回采样器（NUTS）是HMC的变体，能高效选择超参数，但运行慢且不适用于GPU。另一种替代方案——期望平方变化估计量（ChEES-HMC）不仅在GPU上比NUTS运行更快，还表现出更高效的后验采样能力。序贯蒙特卡洛（SMC）采样器通过输出加权样本实现后验近似，易于并行化，适合在GPU上运行，且对提议分布的选择更具灵活性。本文将ChEES-HMC引入SMC采样器中，在多个任务上验证其性能优于或媲美NUTS，同时具有更快的运行速度。
原文摘要 · Abstract (English)
Markov chain Monte Carlo (MCMC) methods are a powerful but computationally expensive way of performing non-parametric Bayesian inference. MCMC proposals which utilise gradients, such as Hamiltonian Monte Carlo (HMC), can better explore the parameter space of interest if the additional hyper-parameters are chosen well. The No-U-Turn Sampler (NUTS) is a variant of HMC which is extremely effective at selecting these hyper-parameters but is slow to run and is not suited to GPU architectures. An alternative to NUTS, Change in the Estimator of the Expected Square HMC (ChEES-HMC) was shown not only to run faster than NUTS on GPU but also sample from posteriors more efficiently. Sequential Monte Carlo (SMC) samplers are another sampling method which instead output weighted samples from the posterior. They are very amenable to parallelisation and therefore being run on GPUs while having additional flexibility in their choice of proposal over MCMC. We incorporate (ChEEs-HMC) as a proposal into SMC samplers and demonstrate competitive but faster performance than NUTS on a number of tasks.
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