On the computation of entropy prior complexity and marginal prior distribution for the Bernoulli model
From MaRDI portal
Publication:2320933
DOI10.1080/15598608.2014.897139zbMath1425.62104OpenAlexW2060044229MaRDI QIDQ2320933
C. Parpoula, Christos Koukouvinos, Narayanaswamy Balakrishnan
Publication date: 28 August 2019
Published in: Journal of Statistical Theory and Practice (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/15598608.2014.897139
Generalized linear models (logistic models) (62J12) Factorial statistical designs (62K15) Statistical aspects of information-theoretic topics (62B10)
Cites Work
- A Mathematical Theory of Communication
- Stochastic complexity and modeling
- Stochastic complexity and model selection from incomplete data
- Choosing the best model: Level of detail, complexity, and model performance
- Generating additive clustering models with minimal stochastic complexity.
- Measures of statistical complexity: why?
- The importance of complexity in model selection
- Analysis of a supersaturated design using entropy prior complexity for binary responses via generalized linear models
- Predictability, Complexity, and Learning
- Optimal Estimation of Parameters
- Statistical Inference, Occam's Razor, and Statistical Mechanics on the Space of Probability Distributions
- On stochastic complexity and nonparametric density estimation
- Model Selection and the Principle of Minimum Description Length
- Probability Theory
- Counting probability distributions: Differential geometry and model selection
- Bayesian Measures of Model Complexity and Fit
- Bayes Factors
- Fisher information and stochastic complexity
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
This page was built for publication: On the computation of entropy prior complexity and marginal prior distribution for the Bernoulli model