Graphs for Margins of Bayesian Networks
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Publication:2821469
DOI10.1111/sjos.12194zbMath1468.62300arXiv1408.1809OpenAlexW1491379284MaRDI QIDQ2821469
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Publication date: 21 September 2016
Published in: Scandinavian Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1408.1809
Bayesian inference (62F15) Applications of graph theory (05C90) Probabilistic graphical models (62H22)
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Uses Software
Cites Work
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- Half-trek criterion for generic identifiability of linear structural equation models
- Statistics, causality and Bell's theorem
- Markovian acyclic directed mixed graphs for discrete data
- Probability distributions with summary graph structure
- Likelihood ratio tests and singularities
- Ancestral graph Markov models.
- Marginalizing and conditioning in graphical models
- Comments on: Sequences of regressions and their independencies
- Margins of discrete Bayesian networks
- Tensors of nonnegative rank two
- On the causal interpretation of acyclic mixed graphs under multivariate normality
- Modeling and Reasoning with Bayesian Networks
- Markov Properties for Acyclic Directed Mixed Graphs
- Factor graphs and the sum-product algorithm
- Independence properties of directed markov fields
- A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect
- Mendelian randomization as an instrumental variable approach to causal inference
- On Dedekind's Problem: The Number of Monotone Boolean Functions
