Model selection by normalized maximum likelihood
From MaRDI portal
Publication:2507907
DOI10.1016/j.jmp.2005.06.008zbMath1100.94008OpenAlexW1999228575MaRDI QIDQ2507907
Jay I. Myung, Mark A. Pitt, Daniel J. Navarro
Publication date: 5 October 2006
Published in: Journal of Mathematical Psychology (Search for Journal in Brave)
Full work available at URL: http://hdl.handle.net/2440/23025
Cognitive psychology (91E10) Information theory (general) (94A15) General considerations in statistical decision theory (62C05)
Related Items
Thou shalt identify! The identifiability of two high-threshold models in confidence-rating recognition (and super-recognition) paradigms, On the normalized maximum likelihood and Bayesian decision theory, A tutorial on Fisher information, Individual differences in the algebraic structure of preferences, The flexibility of models of recognition memory: the case of confidence ratings, Selecting amongst multinomial models: an apologia for normalized maximum likelihood, Length of the state trace: a method for partitioning model complexity, Evaluating models of recognition memory using first- and second-choice responses, The flexibility of models of recognition memory: an analysis by the minimum-description length principle, On the minimum description length complexity of multinomial processing tree models, Model selection by minimum description length: lower-bound sample sizes for the Fisher information approximation, Minimum message length inference of the Poisson and geometric models using heavy-tailed prior distributions, Bayes factors: Prior sensitivity and model generalizability, NML, Bayes and true distributions: a comment on Karabatsos and Walker (2006), Thermodynamic integration and steppingstone sampling methods for estimating Bayes factors: a tutorial
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A universal prior for integers and estimation by minimum description length
- Stochastic complexity and modeling
- Modeling by shortest data description
- Estimating the dimension of a model
- On the complexity of additive clustering models
- Theory of statistics
- An introduction to model selection
- Model selection based on minimum description length
- The importance of complexity in model selection
- Statistical Inference, Occam's Razor, and Statistical Mechanics on the Space of Probability Distributions
- Present Position and Potential Developments: Some Personal Views: Statistical Theory: The Prequential Approach
- Estimating the Error Rate of a Prediction Rule: Improvement on Cross-Validation
- The Validity of the Likelihood Principle
- A Predictive Approach to Model Selection
- Model Selection and the Principle of Minimum Description Length
- Minimum description length induction, Bayesianism, and Kolmogorov complexity
- Strong optimality of the normalized ML models as universal codes and information in data
- MDL denoising
- The minimum description length principle in coding and modeling
- A Note on the Applied Use of MDL Approximations
- Schwarz, Wallace, and Rissanen: Intertwining Themes in Theories of Model Selection
- Bayes Factors
- Fisher information and stochastic complexity
- Minimum Message Length and Kolmogorov Complexity
- Refinements of MDL and MML Coding
- Learning Theory
- An Information Measure for Classification
- The elements of statistical learning. Data mining, inference, and prediction