Search Results for author:
Found 4 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.
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.
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.
Amortized Bayesian model comparison with evidential deep learning
This makes the method particularly effective in scenarios where model fit needs to be assessed for a large number of datasets, so that per-dataset inference is practically infeasible. Finally, we propose a novel way to measure epistemic uncertainty in model comparison problems.
