Computing the maximum-entropy extension of given discrete probability distributions
From MaRDI portal
Publication:804113
DOI10.1016/0167-9473(89)90046-7zbMath0726.62012OpenAlexW1969437862MaRDI QIDQ804113
Publication date: 1989
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/0167-9473(89)90046-7
Approximations to statistical distributions (nonasymptotic) (62E17) Statistical aspects of information-theoretic topics (62B10)
Related Items (9)
Maximum entropy in applied econometric research ⋮ Unnamed Item ⋮ A universal table model for categorical databases ⋮ A note on conditional logics and entropy ⋮ On the effective implementation of the iterative proportional fitting procedure ⋮ Iterative proportional scaling via decomposable submodels for contingency tables ⋮ Unnamed Item ⋮ Decomposition of a hypergraph by partial-edge separators ⋮ An implementation of the iterative proportional fitting procedure by propagation trees.
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A fast algorithm for iterative proportional fitting in log-linear models
- Projection pursuit
- Existence of extensions and product extensions for discrete probability distributions
- Markov fields and log-linear interaction models for contingency tables
- On the Desirability of Acyclic Database Schemes
- Information Theory and Statistical Mechanics
- Simple Linear-Time Algorithms to Test Chordality of Graphs, Test Acyclicity of Hypergraphs, and Selectively Reduce Acyclic Hypergraphs
- Maximum Entropy Condition in Queueing Theory
- Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy
- The Analysis of Multidimensional Contingency Tables: Stepwise Procedures and Direct Estimation Methods for Building Models for Multiple Classifications
- On a Least Squares Adjustment of a Sampled Frequency Table When the Expected Marginal Totals are Known
This page was built for publication: Computing the maximum-entropy extension of given discrete probability distributions