Pages that link to "Item:Q631934"
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The following pages link to A Bayesian hierarchical model for the measurement of working memory capacity (Q631934):
Displaying 16 items.
- Using Bayes factors to test the predictions of models: a case study in visual working memory (Q296960) (← links)
- Drawing conclusions from choice response time models: a tutorial using the linear ballistic accumulator (Q534435) (← links)
- How cognitive modeling can benefit from hierarchical Bayesian models (Q631933) (← links)
- The form of the forgetting curve and the fate of memories (Q631936) (← links)
- Constructing informative model priors using hierarchical methods (Q631951) (← links)
- Generalizing parametric models by introducing trial-by-trial parameter variability: the case of TVA (Q654391) (← links)
- A model of working memory capacity in the radial-arm maze task (Q734678) (← links)
- Hierarchical Bayesian modeling for test theory without an answer key (Q748205) (← links)
- A Bayesian model of capacity across trials (Q825149) (← links)
- Developing memory-based models of ACT-R within a statistical framework (Q826893) (← links)
- Modeling and estimating recall processing capacity: sensitivity and diagnostic utility in application to mild cognitive impairment (Q972221) (← links)
- Statistical measures for workload capacity analysis (Q2438615) (← links)
- Mutual interference in working memory updating: a hierarchical Bayesian model (Q2677693) (← links)
- The E-Bayesian and hierarchical Bayesian estimations for the system reliability parameter (Q2979942) (← links)
- Compression mechanisms in working memory (Q2989940) (← links)
- The M-Bayesian Credible Limits of the Reliability Derived from Binomial Distribution (Q4649604) (← links)
