Bayesian parameter estimation in the Expectancy Valence model of the Iowa gambling task
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Publication:972206
DOI10.1016/j.jmp.2008.12.001zbMath1203.91255OpenAlexW2007962840MaRDI QIDQ972206
Eric‐Jan Wagenmakers, Joachim Vandekerckhove, Francis Tuerlinckx, Ruud Wetzels
Publication date: 25 May 2010
Published in: Journal of Mathematical Psychology (Search for Journal in Brave)
Full work available at URL: https://lirias.kuleuven.be/handle/123456789/216381
Bayesian inference (62F15) Medical applications (general) (92C50) Cognitive psychology (91E10) Rationality and learning in game theory (91A26)
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Cites Work
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- Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
- A statistical model for discriminating between subliminal and near-liminal performance
- A Bayesian analysis of human decision-making on bandit problems
- Tutorial on maximum likelihood estimation
- Modeling individual differences using Dirichlet processes
- Accumulative prediction error and the selection of time series models
- Signal detection models with random participant and item effects
- Problematic effects of aggregation in \(z\)ROC analysis and a hierarchical modeling solution
- Present Position and Potential Developments: Some Personal Views: Statistical Theory: The Prequential Approach
- Transdimensional Markov Chains