Context-Specific and Local Independence in Markovian Dependence Structures

DOI10.1007/978-3-319-31803-5_10zbMath1429.62181OpenAlexW2478632193MaRDI QIDQ5213647

Henrik Nyman, Jukka Corander, Johan Pensar

Publication date: 4 February 2020

Published in: Dependence Logic (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-319-31803-5_10

zbMATH Keywords

marginal likelihoodchordal graphBayesian networkconditional probability distributionleaf node

Mathematics Subject Classification ID

Multivariate distribution of statistics (62H10) Characterization and structure theory for multivariate probability distributions; copulas (62H05) Learning and adaptive systems in artificial intelligence (68T05)

Related Items

Context-Specific Independencies Embedded in Chain Graph Models of Type I ⋮ A logical approach to context-specific independence ⋮ Context-specific independencies in stratified chain regression graphical models

Cites Work

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• Context-specific independence in graphical log-linear models
• Axiomatizing first-order consequences in dependence logic
• Bayesian analysis of variable-order, reversible Markov chains
• Stratified graphical models -- context-specific independence in graphical models
• Inferring decision trees using the minimum description length principle
• A Bayesian method for the induction of probabilistic networks from data
• I-divergence geometry of probability distributions and minimization problems
• Estimating the dimension of a model
• A new algorithm for the maximum likelihood estimation of graphical log-linear models
• Bayesian graphical model determination using decision theory
• Labeled directed acyclic graphs: a generalization of context-specific independence in directed graphical models
• Algorithmic graph theory and perfect graphs
• Learning Bayesian networks: The combination of knowledge and statistical data
• Dependence and independence
• Variable length Markov chains
• Sparse Markov Chains for Sequence Data
• Graphical Models for Marked Point Processes Based on Local Independence
• A universal data compression system
• Labelled Graphical Models
• A universal finite memory source
• Bayesian Networks