THE FUTURE OF COSMOLOGICAL LIKELIHOOD-BASED INFERENCE: ACCELERATED HIGH-DIMENSIONAL PARAMETER ESTIMATION AND MODEL COMPARISON

We advocate for a new paradigm of cosmological likelihood-based inference, leveraging recent developments in machine learning and its underlying technology, to accelerate Bayesian inference in high-dimensional settings.Specifically, we combine (i) emulation, where a machine Fireplace Set learning model is trained to mimic cosmological observables,