Importance Weighting and Variational Inference
Importance Weighting and Variational Inference
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发表时间：
2018-08
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通讯作者：
Justin Domke;D. Sheldon
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作者：
Justin Domke;D. Sheldon
最近的工作使用了重要抽样思想来更好地确定可能性的变分界限。我们通过展示由此产生的重要性加权变分推理（IWVI）技术是增广变分推理的一个实例，从而明确了这些思想对纯概率推理的适用性，从而识别了先前工作中的松散性。实验验证了IWVI在概率推理中的实用性。作为第二个贡献，我们研究了椭圆分布的推理，它提高了低维的精度和高维的收敛性。
Recent work used importance sampling ideas for better variational bounds on likelihoods. We clarify the applicability of these ideas to pure probabilistic inference, by showing the resulting Importance Weighted Variational Inference (IWVI) technique is an instance of augmented variational inference, thus identifying the looseness in previous work. Experiments confirm IWVI's practicality for probabilistic inference. As a second contribution, we investigate inference with elliptical distributions, which improves accuracy in low dimensions, and convergence in high dimensions.