The Fundamental Incompatibility of Hamiltonian Monte Carlo and Data Subsampling

M. J. Betancourt

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arXiv · 1502.01510

The Fundamental Incompatibility of Hamiltonian Monte Carlo and Data Subsampling

M. J. Betancourt

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Abstract

Leveraging the coherent exploration of Hamiltonian flow, Hamiltonian Monte Carlo produces computationally efficient Monte Carlo estimators, even with respect to complex and high-dimensional target distributions. When confronted with data-intensive applications, however, the algorithm may be too expensive to implement, leaving us to consider the utility of approximations such as data subsampling. In this paper I demonstrate how data subsampling fundamentally compromises the efficient exploration of Hamiltonian flow and hence the scalable performance of Hamiltonian Monte Carlo itself.

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M. J. Betancourt. 2015-02-05. The Fundamental Incompatibility of Hamiltonian Monte Carlo and Data Subsampling. https://arxiv.org/abs/1502.01510

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