A Bayesian Method for the Induction of Probabilistic Networks from Data

Machine Learning

Volumn 9, Issue 4, 1992, Pages 309-347

  Cooper, Gregory F ^a   Herskovits, Edward ^b  

^a UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE   (United States)

^b Incorporated ^*  (United States)

Author keywords

Bayesian belief networks; induction; machine learning; probabilistic networks

Indexed keywords

EID: 34249832377     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1023/A:1022649401552     Document Type: Article

References (71)

1. IDES: Influence diagram based expert system
2. MUNIN—A causal probabilistic network for interpretation of electromyographic findings
3. Discovery, confirmation, and incorporation of causal relationships from a large time-oriented clinical database: The RX project
4. Myths and legends in learning classification rules
5. KNET: Integrating hypermedia and normative Bayesian modeling
6. A method of computing generalized Bayesian probability values for expert systems
7. Bayesian classification
8. Approximating discrete probability distributions with dependence trees
9. Current research directions in the development of expert systems based on belief networks
10. Constructor: A system for the induction of probabilistic models
11. Advances in probabilistic reasoning
12. Learning causal trees from dependence information
13. Latent variables, causal models and overidentifying constraints
14. Probabilistic similarity networks
15. Update on the Pathfinder project
16. Propagating uncertainty in Bayesian networks by logic sampling
17. An introduction to algorithms for inference in belief nets
18. An experimental comparison of knowledge engineering for expert systems and for decision analysis
19. Connectionist learning procedures
20. Decision theory in expert systems and artificial intelligence
21. Uncertainty about probability: A decision analysis perspective
22. Recursive causal models
23. Multiple decision trees
24. Fusion, propagation and structuring in belief networks
25. A theory of inferred causality
26. Reconstructability analysis: An overview
27. Induction of decision trees
28. Counting unlabeled acyclic digraphs
29. Intelligent probabilistic inference
30. Probabilistic inference and influence diagrams
31. A linear approximation method for probabilistic inference
32. Gaussian influence diagrams
33. Sequential updating of conditional probabilities on directed graphical structures
34. Causality form probability
35. An algorithm for fast recovery of sparse causal graphs
36. Simulation studies of the reliability of computer-aided model specification using the Tetrad II, EQS, and LISREL programs
37. Probabilistic prediction of the outcome of bone-marrow transplantation
38. Graphical and recursive models for contingency tables