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.

翻译：马尔可夫链蒙特卡洛（MCMC）方法是一种强大但计算成本高昂的非参数贝叶斯推断方法。利用梯度的MCMC提案（如哈密顿蒙特卡洛（HMC））若能恰当选择额外超参数，可更好地探索目标参数空间。无调头采样器（NUTS）作为HMC的一种变体，在超参数选择方面极为有效，但运行速度较慢且不适用于GPU架构。作为NUTS的替代方案，预期平方HMC估计量变化法（ChEES-HMC）被证明不仅在GPU上运行速度优于NUTS，还能更高效地从后验分布中采样。序贯蒙特卡洛（SMC）采样器是另一种采样方法，其输出后验分布的加权样本。该方法极易实现并行化，因此可在GPU上运行，同时在提案分布选择上比MCMC具有更高的灵活性。我们将ChEES-HMC作为提案分布融入SMC采样器，并在多项任务中展示了优于NUTS的竞争性且更快的性能表现。