An Information-Geometric Approach to Learning Bayesian Network Topologies from Data

DOI10.1007/978-3-540-85066-3_8zbMath1187.68400OpenAlexW1492549760MaRDI QIDQ3562271

Eitel J. M. Lauría

Publication date: 21 May 2010

Published in: Innovations in Bayesian Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-540-85066-3_8

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Computing methodologies for information systems (hypertext navigation, interfaces, decision support, etc.) (68U35)

Cites Work

• Optimization by Simulated Annealing
• A Mathematical Theory of Communication
• The imbedding problem for Riemannian manifolds
• Stochastic complexity and modeling
• Modeling by shortest data description
• Bergman-Vekua theory in differential form
• A Bayesian method for the induction of probabilistic networks from data
• Estimating the dimension of a model
• Being Bayesian about network structure. A Bayesian approach to structure discovery in Bayesian networks
• Differential-geometrical methods in statistics
• A Theory of Program Size Formally Identical to Information Theory
• Model Selection and the Principle of Minimum Description Length
• Minimum description length induction, Bayesianism, and Kolmogorov complexity
• Bayes Factors
• Bayesian Graphical Models for Discrete Data
• Fisher information and stochastic complexity
• Learning the Structure of a Bayesian Network: An Application of Information Geometry and the Minimum Description Length Principle
• Equation of State Calculations by Fast Computing Machines
• A formal theory of inductive inference. Part II
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