264 citations · 937 across the 33 of their papers we have counts for
3 papers · 1 filter
Amortized Bayesian model comparison with evidential deep learning
Stefan T. Radev, Marco D'Alessandro, Ulf K. Mertens +3
Comparing competing mathematical models of complex natural processes is a shared goal among many branches of science. The Bayesian probabilistic framework offers a principled way t…
BayesFlow: Learning complex stochastic models with invertible neural networks
Stefan T. Radev, Ulf K. Mertens, Andreas Voss +2
Estimating the parameters of mathematical models is a common problem in almost all branches of science. However, this problem can prove notably difficult when processes and model d…
HINT: Hierarchical Invertible Neural Transport for Density Estimation and Bayesian Inference
Jakob Kruse, Gianluca Detommaso, Ullrich Köthe +1
Many recent invertible neural architectures are based on coupling block designs where variables are divided in two subsets which serve as inputs of an easily invertible (usually af…