Desi R. Ivanova
Desi R. Ivanova
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Type
Conference paper
Date
2021
Implicit Deep Adaptive Design: Policy–Based Experimental Design without Likelihoods
iDAD allows us to practically run Bayesian optimal experiments with implicit (likelihood-free) models in real-time. Previous methods either relied on an explicit likelihood model of the outcomes, or were too computationally costly to run in real-time.
Desi R. Ivanova
,
Adam Foster
,
Steven Kleinegesse
,
Michael Gutmann
,
Tom Rainforth
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Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design
DAD is the first policy-based approach to BOED that enables adaptive experiments to be performed in real-time.
Adam Foster
,
Desi R. Ivanova
,
Ilyas Malik
,
Tom Rainforth
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