Selecting amongst multinomial models: an apologia for normalized maximum likelihood
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Publication:2197107
DOI10.1016/j.jmp.2020.102367zbMath1448.91246OpenAlexW3022114833MaRDI QIDQ2197107
David Kellen, Karl Christoph Klauer
Publication date: 4 September 2020
Published in: Journal of Mathematical Psychology (Search for Journal in Brave)
Full work available at URL: http://psyarxiv.com/9v2hd/
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Cites Work
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- Editors' introduction to the special issue ``Bayes factors for testing hypotheses in psychological research: practical relevance and new developments
- The philosophy of Bayes factors and the quantification of statistical evidence
- Bayes factors, relations to minimum description length, and overlapping model classes
- Thou shalt identify! The identifiability of two high-threshold models in confidence-rating recognition (and super-recognition) paradigms
- Adjusted priors for Bayes factors involving reparameterized order constraints
- The flexibility of models of recognition memory: an analysis by the minimum-description length principle
- The ignored alternative: an application of Luce's low-threshold model to recognition memory
- On the normalized maximum likelihood and Bayesian decision theory
- Testing order constraints: qualitative differences between Bayes factors and normalized maximum likelihood
- The flexibility of models of recognition memory: the case of confidence ratings
- Hierarchical multinomial processing tree models: a latent-trait approach
- Beta-MPT: multinomial processing tree models for addressing individual differences
- On the minimum description length complexity of multinomial processing tree models
- Tree inference with factors selectively influencing processes in a processing tree
- Analysis of multinomial models under inequality constraints: applications to measurement theory
- NML, Bayes and true distributions: a comment on Karabatsos and Walker (2006)
- Estimating the dimension of a model
- A new method for estimating model parameters for multinomial data
- RT-MPTs: process models for response-time distributions based on multinomial processing trees with applications to recognition memory
- Signed difference analysis: testing for structure under monotonicity
- The statistical analysis of general processing tree models with the EM algorithm
- Special issue on model selection
- Generalized processing tree models: jointly modeling discrete and continuous variables
- A simple method for comparing complex models: Bayesian model comparison for hierarchical multinomial processing tree models using Warp-III bridge sampling
- A general approach to prior transformation
- Bayesian estimation of multinomial processing tree models with heterogeneity in participants and items
- Model selection by normalized maximum likelihood
- Model selection by minimum description length: lower-bound sample sizes for the Fisher information approximation
- Bayes factors: Prior sensitivity and model generalizability
- A context-free language for binary multinomial processing tree models
- Model Selection with Informative Normalized Maximum Likelihood: Data Prior and Model Prior
- Statistical Inference, Occam's Razor, and Statistical Mechanics on the Space of Probability Distributions
- Strong optimality of the normalized ML models as universal codes and information in data
- Model Selection and Multimodel Inference
- The minimum description length principle in coding and modeling
- A Note on the Applied Use of MDL Approximations
- Fisher information and stochastic complexity
- An invariant form for the prior probability in estimation problems
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