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Algorithms for likelihood-free cosmological data analysis
25-04-2019
The extraction of physical information from wide and deep astronomical surveys relies on statistical techniques to compare models and observations. A common scenario in cosmology is when we can generate synthetic data through forward simulations, but cannot explicitly formulate the likelihood of the model. The generative process can be extremely general (a noisy non-linear dynamical system involving an unrestricted number of latent variables) and is often computationally expensive. Likelihood-free inference (LFI) provides a framework for performing Bayesian inference in this context, by replacing likelihood calculations with data model evaluations.
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