How hierarchical models improve point estimates of model parameters at the individual level
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Publication:313077
DOI10.1016/j.jmp.2016.03.007zbMath1396.91655OpenAlexW2404113511MaRDI QIDQ313077
Publication date: 9 September 2016
Published in: Journal of Mathematical Psychology (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jmp.2016.03.007
Related Items (6)
Hierarchical Bayesian models of reinforcement learning: introduction and comparison to alternative methods ⋮ Hierarchical multinomial modeling to explain individual differences in children's clustering in free recall ⋮ RT-MPTs: process models for response-time distributions based on multinomial processing trees with applications to recognition memory ⋮ The statistical structures of reinforcement learning with asymmetric value updates ⋮ Decomposition-based gradient estimation algorithms for multivariate equation-error autoregressive systems using the multi-innovation theory ⋮ Biases in estimating the balance between model-free and model-based learning systems due to model misspecification
Uses Software
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- Inference from iterative simulation using multiple sequences
- \({\mathcal Q}\)-learning
- A hierarchical Bayesian statistical framework for response time distributions
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