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Poster
Towards Unifying Hamiltonian Monte Carlo and Slice Sampling
Yizhe Zhang · Xiangyu Wang · Changyou Chen · Ricardo Henao · Kai Fan · Lawrence Carin

Wed Dec 07 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #5
in Wednesday Posters »

We unify slice sampling and Hamiltonian Monte Carlo (HMC) sampling, demonstrating their connection via the Hamiltonian-Jacobi equation from Hamiltonian mechanics. This insight enables extension of HMC and slice sampling to a broader family of samplers, called Monomial Gamma Samplers (MGS). We provide a theoretical analysis of the mixing performance of such samplers, proving that in the limit of a single parameter, the MGS draws decorrelated samples from the desired target distribution. We further show that as this parameter tends toward this limit, performance gains are achieved at a cost of increasing numerical difficulty and some practical convergence issues. Our theoretical results are validated with synthetic data and real-world applications.

Keywords: (Other) Statistics · MCMC · (Other) Bayesian Inference
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Author Information

Yizhe Zhang (Duke university)
Xiangyu Wang (Duke University)
Changyou Chen (University at Buffalo)
Ricardo Henao (Duke University)
Kai Fan (Duke university)
Lawrence Carin (KAUST)

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