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Fast likelihood-free cosmology with neural density estimators and active learning

By Justin Alsing and others
Likelihood-free inference provides a framework for performing rigorous Bayesian inference using only forward simulations, properly accounting for all physical and observational effects that can be successfully included in the simulations. The key challenge for likelihood-free applications in cosmology, where simulation is typically expensive, is developing methods that can achieve high-fidelity... Show more
February 28, 2019
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Fast likelihood-free cosmology with neural density estimators and active learning
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