Add to ORKGUsing Bayesian hierarchical parameter estimation to assess the generalizability of cognitive models of choic
Results of Bayesian hierarchical modeling with the use of metacognitive strategies as the dependent ...
An important problem for HCI researchers is to estimate the parameter values of a cognitive model fr...
Approximate Bayesian computation (ABC) is a powerful technique for estimating the posterior dis-trib...
Abstract To be useful, cognitive models with fitted parame-ters should show generalizability across ...
Introduction: The need for hierarchical models Those of us who study human cognition have no easy ta...
Bayesian data analysis involves describing data by meaningful mathematical models, and allocating cr...
To be useful, cognitive models with fitted parameters should show generalizability across time and a...
Results of Bayesian hierarchical modeling with the use of cognitive strategies as the dependent vari...
The hierarchical Bayesian approach to cognitive modeling often provides a quality of inference that ...
From a computational perspective, the primary goal of cognitive science is to infer the influence of...
Parametric cognitive models are increasingly popular tools for analyzing data obtained from psycholo...
Parametric cognitive models are increasingly popular tools for analyzing data obtained from psycholo...
Parametric cognitive models are increasingly popular tools for analyzing data obtained from psycholo...
Contains fulltext : 205646.pdf (publisher's version ) (Open Access
Model comparison is the cornerstone of theoretical progress in psychological research. Common practi...
Results of Bayesian hierarchical modeling with the use of metacognitive strategies as the dependent ...
An important problem for HCI researchers is to estimate the parameter values of a cognitive model fr...
Approximate Bayesian computation (ABC) is a powerful technique for estimating the posterior dis-trib...
Abstract To be useful, cognitive models with fitted parame-ters should show generalizability across ...
Introduction: The need for hierarchical models Those of us who study human cognition have no easy ta...
Bayesian data analysis involves describing data by meaningful mathematical models, and allocating cr...
To be useful, cognitive models with fitted parameters should show generalizability across time and a...
Results of Bayesian hierarchical modeling with the use of cognitive strategies as the dependent vari...
The hierarchical Bayesian approach to cognitive modeling often provides a quality of inference that ...
From a computational perspective, the primary goal of cognitive science is to infer the influence of...
Parametric cognitive models are increasingly popular tools for analyzing data obtained from psycholo...
Parametric cognitive models are increasingly popular tools for analyzing data obtained from psycholo...
Parametric cognitive models are increasingly popular tools for analyzing data obtained from psycholo...
Contains fulltext : 205646.pdf (publisher's version ) (Open Access
Model comparison is the cornerstone of theoretical progress in psychological research. Common practi...
Results of Bayesian hierarchical modeling with the use of metacognitive strategies as the dependent ...
An important problem for HCI researchers is to estimate the parameter values of a cognitive model fr...
Approximate Bayesian computation (ABC) is a powerful technique for estimating the posterior dis-trib...