Graphical Models for Probabilistic and Causal Reasoning

@inproceedings{Pearl1997GraphicalMF,
  title={Graphical Models for Probabilistic and Causal Reasoning},
  author={Judea Pearl},
  booktitle={The Computer Science and Engineering Handbook},
  year={1997}
}
  • Judea Pearl
  • Published 1997 in The Computer Science and Engineering Handbook
This chapter surveys the development of graphical models known as Bayesian networks, summarizes their semantical basis and assesses their properties and applications to reasoning and planning. Bayesian networks are directed acyclic graphs (DAGs) in which the nodes represent variables of interest (e.g., the temperature of a device, the gender of a patient, a feature of an object, the occurrence of an event) and the links represent causal influences among the variables. The strength of an… CONTINUE READING
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References

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Showing 1-10 of 67 references

Introduction to Structural Equation Models

  • O. Duncan
  • Academic Press, New York.
  • 1975
Highly Influential
9 Excerpts

From Adams’ conditionals to default expressions, causal conditionals, and counterfactuals

  • J. Pearl
  • Probability and Conditionals (E. Eells and B…
  • 1994
Highly Influential
7 Excerpts

Graphical Models

  • S. Lauritzen
  • Clarendon Press, Oxford.
  • 1996
1 Excerpt

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