10 citations · 20 across the 4 of their papers we have counts for
5 papers
Neural Superstatistics for Bayesian Estimation of Dynamic Cognitive Models
Lukas Schumacher, Paul-Christian Bürkner, Andreas Voss +2
Mathematical models of cognition are often memoryless and ignore potential fluctuations of their parameters. However, human cognition is inherently dynamic. Thus, we propose to aug…
Amortized Bayesian Inference for Models of Cognition
Stefan T. Radev, Andreas Voss, Eva Marie Wieschen +1
As models of cognition grow in complexity and number of parameters, Bayesian inference with standard methods can become intractable, especially when the data-generating model is of…
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…
A Bayesian brain model of adaptive behavior: An application to the Wisconsin Card Sorting Task
Marco D'Alessandro, Stefan T. Radev, Andreas Voss +1
Adaptive behavior emerges through a dynamic interaction between cognitive agents and changing environmental demands. The investigation of information processing underlying adaptive…
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…