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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.
Constraining the Reionization History using Bayesian Normalizing Flows
Additionally, we demonstrate the advantages of Normalizing Flows (NF) combined with BNNs, being able to model more complex output distributions and thus capture key information as non-Gaussianities in the parameter conditional density distribution for astrophysical and cosmological dataset.
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