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Found 3 papers, 3 papers with code
Validation and Comparison of Non-Stationary Cognitive Models: A Diffusion Model Application
To validate our models, we assess whether the inferred parameter trajectories align with the patterns and sequences of the experimental manipulations.
BayesFlow: Amortized Bayesian Workflows With Neural Networks
Modern Bayesian inference involves a mixture of computational techniques for estimating, validating, and drawing conclusions from probabilistic models as part of principled workflows for data analysis.
Neural Superstatistics for Bayesian Estimation of Dynamic Cognitive Models
Our results show that the deep learning approach is very efficient in capturing the temporal dynamics of the model.
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