Combining knowledge from different sources in causal probabilistic models

Journal of Machine Learning Research

Volumn 4, Issue 3, 2004, Pages 295-316

  Druzdzel, Marek J ^a   Díez, Francisco J ^b  

^a UNIVERSITY OF PITTSBURGH   (United States)

^b UNIVERSIDAD NACIONAL DE EDUCACIÓN A DISTANCIA   (Spain)

Author keywords

Bayesian networks; Combining knowledge; Elicitation; Learning; Numerical probabilities; Probabilistic models; Selection biases

Indexed keywords

ALGORITHMS; DATA MINING; DATABASE SYSTEMS; DECISION MAKING; GRAPH THEORY; KNOWLEDGE BASED SYSTEMS; MATHEMATICAL MODELS; PARAMETER ESTIMATION; PROBABILITY DISTRIBUTIONS; STATISTICAL METHODS; USER INTERFACES;

BAYESIAN NETWORKS; COMBINING KNOWLEDGE; ELICITATION; LEARNING; NUMERICAL PROBABILITIES; PROBABILISTIC MODELS; SELECTION BIASES;

KNOWLEDGE ENGINEERING;

EID: 2342486104     PISSN: 15324435     EISSN: None     Source Type: Journal    
DOI: 10.1162/153244304773633834     Document Type: Article

References (22)

1. J. Berkson. Limitations of the application of fourfold table analysis to hospital data. Biometrics, 2: 47-53, 1946.
2. G. F. Cooper and E. Herskovits. A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9(4):309-347, 1992.
3. F. J. Díez, M. Druzdzel, and M. A. Hernán. Biases in the assessment of diagnostic indicants. Technical Report, UNED, Madrid, 2003. In preparation.
4. F. J. Díez, J. Mira, E. Iturralde, and S. Zubillaga. DIAVAL, a Bayesian expert system for echocardiography. Artificial Intelligence in Medicine, 10:59-73, 1997.
5. M. J. Druzdzel and L. C. van der Gaag. Building probabilistic networks: "Where do the numbers come from?" Guest editors' introduction. IEEE Transactions on Knowledge and Data Engineering, 12(4):481-486, July-August 2000.
6. W. K. Estes. The cognitive side of probability learning. Psychological Review, 83(1):37-64, January 1976.
7. S. Greenland, J. Pearl, and J. M. Robins. Causal diagrams for epidemiologic research. Epidemiology, 10:37-48, 1999.
8. M. Henrion, J. S. Breese, and E. J. Horvitz. Decision Analysis and Expert Systems. AI Magazine, 12(4):64-91, Winter 1991.
9. M. A. Hernán, S. Hernández-Díaz, M. M. Werler, and A. A. Mitchell. Causal knowledge as a prerequisite for confounding evaluation: an application to birth defects epidemiology. American Journal of Epidemiology, 155:176-184, 2002.
10. R. A. Howard and J. E. Matheson. Influence diagrams. In Ronald A. Howard and James E. Matheson, editors, The Principles and Applications of Decision Analysis, pages 719-762. Strategic Decisions Group, Menlo Park, CA, 1984.
11. D. Kahneman, P. Slovic, and A. Tversky, editors. Judgment Under Uncertainty: Heuristics and Biases. Cambridge University Press, Cambridge, 1982.
12. J. A. Knottnerus. The effects of disease verification and referral on the relationship between symptoms and diseases. Medical Decision Making, 7:139-148, 1987.
13. C. Lacave and F. J. Díez. A review of explanation methods for Bayesian networks. Knowledge Engineering Review, 17:107-127, 2002.
14. P. Macaskill, S. D. Walter, and L. Irwig. A comparison of methods to detect publication bias in meta-analysis. Statistics in Medicine, 20:641-654, 2001.
15. A. Oniśko, M. J. Druzdzel, and H. Wasyluk. Learning Bayesian network parameters from small data sets: Application of Noisy-OR gates. International Journal of Approximate Reasoning, 27 (2):165-182, 2001.
16. J. Pearl. Causality. Models, Reasoning, and Inference. Cambridge University Press, Cambridge, UK, 2000.
17. J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, Inc., San Mateo, CA, 1988.
18. J. Pearl and T. S. Verma. A theory of inferred causation. In J.A. Alien, R. Fikes, and E. Sandewall, editors, KR-91, Principles of Knowledge Representation and Reasoning: Proceedings of the Second International Conference, pages 441-452, Cambridge, MA, 1991. Morgan Kaufmann Publishers, Inc., San Mateo, CA.
19. D. F. Ransohoff and A. R. Feinstein. Problems of spectrum and bias in evaluating the efficacy of diagnostic tests. New England Journal of Medicine, 299:926-930, 1978.
20. D. L. Sackett, W. S. Richardson, W. Rosenberg, and R. B. Haynes. Evidence-Based Medicine. How to Practice and Teach EBM. Churchill Livingstone, New York, 1997.
21. P. Spirtes, C. Glymour, and R. Scheines. Causation, Prediction, and Search. Springer Verlag, New York, 1993.
22. Y. T. van der Schouw, R. van Dijk, and A. L. M. Verbeek. Problems in selecting the adequate patient population from existing data files for assessment studies of new diagnostic tests. Journal of Clinical Epidemiology, 48:417-422, 1995.