A stochastic version of Stein Variational Gradient Descent for efficient sampling
We propose in this work RBM-SVGD, a stochastic version of Stein Variational Gradient Descent (SVGD) method for efficiently sampling from a given probability measure and thus useful for Bayesian inference. The method is to apply the Random Batch Method (RBM) for interacting particle systems proposed...
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| creator | Li, Lei Li, Yingzhou Jian-Guo, Liu Liu, Zibu Lu, Jianfeng |
| description | We propose in this work RBM-SVGD, a stochastic version of Stein Variational Gradient Descent (SVGD) method for efficiently sampling from a given probability measure and thus useful for Bayesian inference. The method is to apply the Random Batch Method (RBM) for interacting particle systems proposed by Jin et al to the interacting particle systems in SVGD. While keeping the behaviors of SVGD, it reduces the computational cost, especially when the interacting kernel has long range. Numerical examples verify the efficiency of this new version of SVGD. |
| doi_str_mv | 10.48550/arxiv.1902.03394 |
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The method is to apply the Random Batch Method (RBM) for interacting particle systems proposed by Jin et al to the interacting particle systems in SVGD. While keeping the behaviors of SVGD, it reduces the computational cost, especially when the interacting kernel has long range. Numerical examples verify the efficiency of this new version of SVGD.</description><identifier>EISSN: 2331-8422</identifier><identifier>DOI: 10.48550/arxiv.1902.03394</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Batch processing ; Bayesian analysis ; Computer Science - Learning ; Mathematics - Probability ; Probabilistic inference ; Sampling ; Statistical inference ; Statistics - Machine Learning</subject><ispartof>arXiv.org, 2019-04</ispartof><rights>2019. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). 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| subjects | Batch processing Bayesian analysis Computer Science - Learning Mathematics - Probability Probabilistic inference Sampling Statistical inference Statistics - Machine Learning |
| title | A stochastic version of Stein Variational Gradient Descent for efficient sampling |
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