Amortized Bayesian Inference for Models of Cognition

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 unknown analytic form. Recent advances in simulation-based inference using specialized neural network architectures cir...

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Veröffentlicht in:	arXiv.org 2020-07
Hauptverfasser:	Radev, Stefan T, Voss, Andreas, Wieschen, Eva Marie, Paul-Christian Bürkner
Format:	Artikel
Sprache:	eng
Schlagworte:	Bayesian analysis Cognition Computer architecture Computer simulation Mathematical models Neural networks Parameter estimation Psychology Statistical inference
Online-Zugang:	Volltext
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