Amortized Monte Carlo integration
Current approaches to amortizing Bayesian inference focus solely on approximating the posterior distribution. Typically, this approximation is, in turn, used to calculate expectations for one or more target functions{—}a computational pipeline which is inefficient when the target function(s) are kno...
| 主要な著者: | , , |
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| フォーマット: | Conference item |
| 出版事項: |
Proceedings of Machine Learning Research
2019
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