Parallel-tempered Stochastic Gradient Hamiltonian Monte Carlo for Approximate Multimodal Posterior Sampling

Rui Luo; Qiang Zhang; Yuanyuan Liu

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arXiv · 1812.01181

Parallel-tempered Stochastic Gradient Hamiltonian Monte Carlo for Approximate Multimodal Posterior Sampling

Rui Luo·Qiang Zhang·Yuanyuan Liu

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Abstract

We propose a new sampler that integrates the protocol of parallel tempering with the Nos\'e-Hoover (NH) dynamics. The proposed method can efficiently draw representative samples from complex posterior distributions with multiple isolated modes in the presence of noise arising from stochastic gradient. It potentially facilitates deep Bayesian learning on large datasets where complex multimodal posteriors and mini-batch gradient are encountered.

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BibTeX RIS

Rui Luo, Qiang Zhang, Yuanyuan Liu. 2018-12-04. Parallel-tempered Stochastic Gradient Hamiltonian Monte Carlo for Approximate Multimodal Posterior Sampling. https://arxiv.org/abs/1812.01181

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