We consider versions of the Metropolis algorithm which avoid the inefficiency of rejections. We first illustrate that a natural Uniform Selection algorithm might not converge to the correct distribution. We then analyse the use of Markov jump chains which avoid successive repetitions of the same state. After exploring the properties of jump chains, we show how they can exploit parallelism in computer hardware to produce more efficient samples. We apply our results to the Metropolis algorithm, to Parallel Tempering, to a Bayesian model, to a two-dimensional ferromagnetic 4\(\times \)4 Ising model, and to a pseudo-marginal MCMC algorithm.

我们考虑了Metropolis算法的版本，这些版本可避免拒绝效率低下的情况。我们首先说明自然的均匀选择算法可能不会收敛到正确的分布。然后，我们分析了马尔可夫跳链的使用，这些跳链避免了相同状态的连续重复。在研究了跳链的属性之后，我们展示了跳链如何利用计算机硬件中的并行性来产生更有效的样本。我们将结果应用到Metropolis算法，并行回火，贝叶斯模型，二维铁磁4 \（\ times \） 4 Ising模型以及伪边际MCMC算法。