BPS-PT, an infinite-swapping parallel tempering variant of the bouncy particle sampler, improves mixing on a multimodal Gaussian mixture and a mixed discrete-continuous model, with large compute overhead.
Metropolis Augmented Hamiltonian Monte Carlo
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abstract
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.
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Bouncy particle sampler with infinite exchanging parallel tempering
BPS-PT, an infinite-swapping parallel tempering variant of the bouncy particle sampler, improves mixing on a multimodal Gaussian mixture and a mixed discrete-continuous model, with large compute overhead.