Variational Bayesian Optimal Experimental Design
Variational Bayesian Optimal Experimental Design
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发表时间：
2019-03
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通讯作者：
Adam Foster;M. Jankowiak;Eli Bingham;Paul Horsfall;Y. Teh;Tom Rainforth;Noah D. Goodman
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作者：
Adam Foster;M. Jankowiak;Eli Bingham;Paul Horsfall;Y. Teh;Tom Rainforth;Noah D. Goodman
贝叶斯优化实验设计（BOED）是有效利用有限实验资源的一个原则性框架。不幸的是，它的适用性受到难以准确估计实验的预期信息增益（EIG）的阻碍。为了解决这个问题，我们在平摊变分推理的基础上引入了几类快速EIG估计器。我们从理论上和经验上证明，这些估计器比以前的方法在速度和准确性上都有显著的提高。我们在一些端到端实验中进一步证明了我们的方法的实用性。
Bayesian optimal experimental design (BOED) is a principled framework for making efficient use of limited experimental resources. Unfortunately, its applicability is hampered by the difficulty of obtaining accurate estimates of the expected information gain (EIG) of an experiment. To address this, we introduce several classes of fast EIG estimators by building on ideas from amortized variational inference. We show theoretically and empirically that these estimators can provide significant gains in speed and accuracy over previous approaches. We further demonstrate the practicality of our approach on a number of end-to-end experiments.