A recovery algorithm for chain graphs
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TL;DR: A recovery algorithm is presented, which on the basis of the conditional independence structure given by a CG (in the form of a dependency model) finds the largest CG representing the corresponding class of Markov equivalent CGs.
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About: This article is published in International Journal of Approximate Reasoning. The article was published on 01 Aug 1997. and is currently open access. The article focuses on the topics: Markov chain & Variable-order Markov model.
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Citations
•Proceedings Article
Bayesian networks from the point of view of chain graphs
Milan Studený
- 24 Jul 1998
TL;DR: The paper gives a few arguments in favour of use of chain graphs for description of probabilistic conditional independence structures and a separation criterion for reading independences from a chain graph is formulated.
43
A graphical characterization of the largest chain graphs
Martin Volf,Milan Studený +1 more
TL;DR: The paper presents a graphical characterization of the largest chain graphs which serve as unique representatives of classes of Markov equivalent chain graphs, and is a basis for an algorithm constructing, for a given chain graph, thelargest chain graph equivalent to it.
41
A Unified Approach to the Characterization of Equivalence Classes of DAGs, Chain Graphs with no Flags and Chain Graphs
TL;DR: In this paper, the existence of the largest CGs with no flags was shown to be a natural characterization of equivalence classes of chain graphs of this kind, with respect to both the LWF- and the AMP-Markov properties.
33
Characterizing Markov equivalence classes for AMP chain graph models
TL;DR: In this paper, the AMP essential graph (AMP) is defined for chain graphs and shown to be simultaneously Markov equivalent to all chain graphs in AMP Markov equivalence class.
•Journal Article
A Graphical Representation of Equivalence Classes of AMP Chain Graphs
Alberto Roverato,Milan Studený +1 more
TL;DR: This paper deals with chain graph models under alternative AMP interpretation with a new representative of an AMP Markov equivalence class, called the largest deflagged graph, based on revealed internal structure of the AMPMarkov equivalences class.
References
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Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Judea Pearl
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TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.
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Probabilistic reasoning in intelligent systems: Networks of plausible inference
TL;DR: Probabilistic methods to create the areas, of computational tools, and apparently daphne koller and learning structures evidential reasoning, Pearl is a language for i've is not great give the best references.
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Uncertainty in Artificial Intelligence 2
L. N. Kanal,J. F. Lemmer,Andrew P. Sage +2 more
- 01 Jan 1988
TL;DR: Qualitative Probabilistic Reasoning and Cognitive models, Dempster-Shafer Theory in Knowledge Representation, and Possibility Theory: Semantics and Applications.
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Equivalence and synthesis of causal models
Thomas Verma,Judea Pearl +1 more
- 27 Jul 1990
TL;DR: The canonical representation presented here yields an efficient algorithm for determining when two embedded causal models reflect the same dependency information, which leads to a model theoretic definition of causation in terms of statistical dependencies.
1.4K
Graphical Models for Associations between Variables, some of which are Qualitative and some Quantitative
TL;DR: In this article, the authors define and investigate classes of statistical models for the analysis of associations between variables, some of which are qualitative and some quantitative, and characterize the subclass of decomposable models where the statistical theory is especially simple.
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