On predictive distributions and Bayesian networks

Statistics and Computing

Volumn 10, Issue 1, 2000, Pages 39-54

  Kontkanen, P ^a   Myllymaki P ^a   Silander, T ^a   Tirri, H ^a   Grunwald P ^b  

^a UNIVERSITY OF HELSINKI   (Finland)

^b Stanford University   (United States)

Author keywords

Bayesian networks; Jeffreys' prior; MDL; MML; Predictive inference

Indexed keywords

EID: 0041683329     PISSN: 09603174     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1008984400380     Document Type: Article

References (36)

1. Baxter R. and Oliver J. 1994. MDL and MML: similarities and differences. Technical Report 207, Department of Computer Science, Monash University.
2. Berger J. 1985. Statistical Decision Theory and Bayesian Analysis. New York, Springer-Verlag.
3. Bernardo J. and Smith A. 1994. Bayesian theory. John Wiley.
4. Blake C., Keogh E., and Merz C. 1998. UCI repository of machine learning databases. URL: http://www.ics.uci.edu/~mlearn/MLRepository.html.
5. Castillo E., Gutiérrez J., and Hadi A. 1997. Expert Systems and Probabilistic Network Models, Monographs in Computer Science. New York, NY, Springer-Verlag.
6. Clarke B. and Barron A. 1990. Information-theoretic asymptotics of Bayes methods. IEEE Transactions on Information Theory 36(3): 453-471.
7. Clarke B. and Barron A. 1994. Jeffrey's Prior is asymptotically least favorable under entropy risk. Journal of Statistical Planning and Inference 41: 37-60.
8. Cooper G. 1990. The computational complexity of probabilistic inference using Bayesian belief networks. Artificial Intelligence 42(2-3): 393-405.
9. Cooper G. and Herskovits E. 1992. A Bayesian method for the induction of probabilistic networks from data. Machine Learning 9: 309-347.
10. Cover T. and Thomas J. 1991. Elements of Information Theory. New York, NY, John Wiley & Sons.
11. DeGroot M. 1970. Optimal Statistical Decisions. McGraw-Hill.
12. Dom B. 1995. MDL estimation with small sample sizes including an application to the problem of segmenting binary strings using Bernoulli models. Technical Report RJ 9997 (89085), IBM Research Division, Almaden Research Center.
13. Friedman N. and Goldszmidt 1996. Building classifiers using Bayesian networks. In: Proceedings of the Thirteenth National Conference on Artificial Intelligence, Portland, Oregon, pp. 1277-1284.
14. Geiger D. and Heckerman D. 1994. A characterization of the Dirichlet distribution through global and local independence. Technical Report MSR-TR-94-16, Microsoft Research.
15. Grünwald P. 1998. The minimum description length principle and reasoning under uncertainty. Ph.D. Thesis, CWI, ILLC Dissertation Series 1998-03.
16. Grünwald P., Kontkanen P., Myllymäki P., Silander T., and Tirri H. 1998. Minimum encoding approaches for predictive modeling. In: Cooper G. and Moral S. (Eds.), Proceedings of the 14th International Conference on Uncertainty in Artificial Intelligence (UAI'98), Madison, WI, pp. 183-192.
17. Heckerman D., Geiger D., and Chickering D. 1995. Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning 20(3): 197-243.
18. Jensen F. 1996. An Introduction to Bayesian Networks. London, UCL Press.
19. Kass R. and Voss P. 1997. Geometrical Foundations of Asymptotic Inference. Wiley Interscience.
20. Kontkanen P., Myllymäki P., Silander T., Tirri H., and Grünwald P. 1997. Comparing predictive inference methods for discrete domains. In: Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics, Ft. Lauderdale, Florida, pp. 311-318.
21. Kontkanen P., Myllymäki P., Silander T., Tirri H., and Valtonen K. 1999. Exploring the robustness of Bayesian and information-theoretic methods for predictive inference. In: Heckerman D. and Whittaker J. (Eds.), Proceedings of Uncertainty'99: The Seventh International Workshop on Artificial Intelligence and Statistics, Morgan Kaufmann Publishers, pp. 231-236.
22. Langley P. and Sage S. 1994 Induction of selective Bayesian classifiers. In: Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, Seattle, Oregon, pp. 399-406.
23. Michie D., Spiegelhalter D., and Taylor C. (Eds.), 1994. Machine Learning, Neural and Statistical Classification, London, Ellis Horwood.
24. Neapolitan R. 1990. Probabilistic Reasoning in Expert Systems. New York, NY, John Wiley & Sons.
25. Pearl J. 1988. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Mateo, CA, Morgan Kaufmann Publishers.
26. Rissanen J. 1987. Stochastic complexity. Journal of the Royal Statistical Society 49(3): 223-239 and 252-265.
27. Rissanen J. 1989. Stochastic Complexity in Statistical Inquiry. New Jersey, World Scientific Publishing Company.
28. Rissanen J. 1996. Fisher information and stochastic complexity. IEEE Transactions on Information Theory 42(1): 40-47.
29. Shachter R. 1988. Probabilistic inference and influence diagrams. Operations Research 36(4): 589-604.
30. Takeuchi J. and Barron A. 1998. Asymptotically minimax regret by Bayes mixtures. In: 1998 IEEE International Symposium on Information Theory. Cambridge, MA, August 1998.
31. Thiesson B. 1995. Score and information for recursive exponential models with incomplete data. Technical Report R-95-2020, Aalborg University, Institute for Electronic Systems, Department of Mathematics and Computer Science.
32. Tirri H., Kontkanen P., and Myllymäki P. 1996. Probabilistic instance-based learning. In: Saitta L. (Ed.), Machine Learning: Proceedings of the Thirteenth International Conference (ICML'96), pp. 507-515.
33. Wallace C. and Boulton D. 1968. An information measure for classification. Computer Journal 11: 185-194.
34. Wallace C. and Freeman P. 1987. Estimation and inference by compact coding. Journal of the Royal Statistical Society 49(3): 240-265.
35. Wallace C., Korb K., and Dai H. 1996a. Causal discovery via MML. Technical Report 96/254, Department of Computer Science, Monash University.
36. Wallace C., Korb K., and Dai H. 1996b. Causal discovery via MML. In: Saitta L. (Ed.), Machine Learning: Proceedings of the Thirteenth International Conference (ICML'96), pp. 516-524.