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Variational Bayesian Optimal Experimental Design
Bayesian optimal experimental design (BOED) is a principled framework fo...
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Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design
We introduce Deep Adaptive Design (DAD), a general method for amortizing...
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A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments
We introduce a fully stochastic gradient based approach to Bayesian opti...
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Sequential Bayesian Experimental Design for Implicit Models via Mutual Information
Bayesian experimental design (BED) is a framework that uses statistical ...
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Efficient Bayesian Experimental Design for Implicit Models
Bayesian experimental design involves the optimal allocation of resource...
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Neural-Network Heuristics for Adaptive Bayesian Quantum Estimation
Quantum metrology promises unprecedented measurement precision but suffe...
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Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation
Implicit stochastic models, where the data-generation distribution is in...
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Variational Estimators for Bayesian Optimal Experimental Design
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 suited to the experiment design context by building on ideas from variational inference and mutual information estimation. We show theoretically and empirically that these estimators can provide significant gains in speed and accuracy over previous approaches. We demonstrate the practicality of our approach via a number of experiments, including an adaptive experiment with human participants.
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