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Found 4 papers, 1 papers with code
Constraining cosmological parameters from N-body simulations with Bayesian Neural Networks
In this paper, we use The Quijote simulations in order to extract the cosmological parameters through Bayesian Neural Networks.
Accelerating MCMC algorithms through Bayesian Deep Networks
Markov Chain Monte Carlo (MCMC) algorithms are commonly used for their versatility in sampling from complicated probability distributions.
Reliable Uncertainties for Bayesian Neural Networks using Alpha-divergences
Bayesian Neural Networks (BNNs) often result uncalibrated after training, usually tending towards overconfidence.
Parameters Estimation for the Cosmic Microwave Background with Bayesian Neural Networks
In the second part of the paper, we present a guide to the training and calibration of a successful multi-channel BNN for the CMB temperature and polarization map.
