论文标题
大都会增强汉密尔顿蒙特卡洛
Metropolis Augmented Hamiltonian Monte Carlo
论文作者
论文摘要
汉密尔顿蒙特卡洛（HMC）是一种强大的马尔可夫链蒙特卡洛（MCMC）方法，用于从复杂的高维连续分布中取样。但是，在许多情况下，有必要或希望将HMC与其他大都市（MH）采样器相结合。常见的HMC-Within-GIBBS策略意味着长时间的HMC轨迹与更频繁的其他MH更新之间的权衡。解决这一权衡是最近几项工作的重点。在本文中，我们建议大都市增强汉密尔顿蒙特卡洛（MAHMC），这是一种HMC变体，允许HMC内的MH更新并消除这种权衡。与GIBBS替代方案相比，两个代表性示例的实验证明了MAHMC的效率和易用性。
Hamiltonian Monte Carlo (HMC) is a powerful Markov Chain Monte Carlo (MCMC) method for sampling from complex high-dimensional continuous distributions. However, in many situations it is necessary or desirable to combine HMC with other Metropolis-Hastings (MH) samplers. The common HMC-within-Gibbs strategy implies a trade-off between long HMC trajectories and more frequent other MH updates. Addressing this trade-off has been the focus of several recent works. In this paper we propose Metropolis Augmented Hamiltonian Monte Carlo (MAHMC), an HMC variant that allows MH updates within HMC and eliminates this trade-off. Experiments on two representative examples demonstrate MAHMC's efficiency and ease of use when compared with within-Gibbs alternatives.
