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Activity Number:
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254
- Contributed Poster Presentations: Section on Bayesian Statistical Science
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Type:
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Contributed
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Date/Time:
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Monday, July 29, 2019 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #307230
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Title:
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Stein Neural Sampler
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Author(s):
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Tianyang Hu* and Zixiang Chen and Hanxi Sun and Jincheng Bai and Mao Ye and Guang Cheng
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Companies:
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Purdue Statistics and Tsinghua Statistics and Purdue Statistics and Purdue Statistics and Purdue Statistics and Purdue Statistics
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Keywords:
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Sampling;
GAN;
Approximate Inference
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Abstract:
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We propose two novel sampling methods to generate high-quality samples from a given (un-normalized) probability density. Motivated by the success of generative adversarial networks, we construct our samplers using neural networks that transform a reference distribution to the target distribution. Training schemes are developed to minimize two variations of Stein discrepancy, which is designed to work with un-normalized densities. Once trained, our samplers are able to generate samples instantaneously. Theoretical and empirical results suggest that our methods are asymptotically correct and experience fewer convergence issues in practice compared with traditional sampling approaches.
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Authors who are presenting talks have a * after their name.
