Appendices: References

From DSL

1. E. Charniak (1991), Bayesian Networks without Tears. AI Magazine, 12(4), 50-63.

2. J. Cheng and M.J. Druzdzel (2000), AIS-BN: An adaptive importance sampling algorithm for evidential reasoning in large Bayesian networks. Journal of Artificial Intelligence Research (JAIR), 13:155-188.

3. G. F. Cooper (1987), Probabilistic Inference Using Belief Networks is NP-hard. Report KSL-87-27, Medical Computer Science Group, Stanford University, Stanford, California.

4. G. F. Cooper (1988), A Method for Using Belief Networks Algorithms to Solve Decision-Network Problems. Proceedings of the 1988 AAAI Workshop on Uncertainty in Artificial Intelligence, Minneapolis MN.

5. P. Dagum and M. Luby (1993), Approximating Probabilistic Inference in Bayesian Belief Networks is NP-Hard. Artificial Intelligence, 60, 141-153.

6. M.J. Druzdzel and H.J. Suermondt (1994), Relevance in Probabilistic Models: "Backyards" in a "Small World". In Working notes of the AAAI-1994 Fall Symposium Series: Relevance, New Orleans, LA, pages 60-63.

7. R. Fung, and K. Chang (1990), Weighting and Integrating Evidence for Stochastic Simulation in Bayesian Networks. In M. Henrion and R.D. Shachter and L.N. Kanal and J.F. Lemmer (Eds.) Uncertainty in Artificial Intelligence, 5. North Holland. 209-219.

8. R. Fung and B. Del Favero (1994), Backward Simulation in Bayesian Networks. Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, 227-234. San Francisco, CA.

9. M. Henrion (1988), Propagating Uncertainty in Bayesian Networks by Probabilistic Logic Sampling. In Lemmer, J.F. and Kanal, L.N. (Eds.) Uncertainty in Artificial Intelligence, 2. North Holland. 149-163.

10. M. Henrion, J.S. Breese, and E.J. Horvitz (1991), Decision Analysis and Expert Systems. AI Magazine, 12(4), 64-91.

11. R.A. Howard and J.E. Matheson (1984), Readings on the Principles and Applications of Decision Analysis. Strategic Decisions Group, Menlo Park, California.

12. C. Huang, and A. Darwiche (1996), Inference in Belief Networks: A Procedural Guide. International Journal of Approximate Reasoning, 15, 225-263.

13. F. Jensen and S.K. Andersen (1990), Approximations in Bayesian Belief Universes for Knowledge-based Systems. In Proceedings of the 6th Conference on Uncertainty in Artificial Intelligence, Cambridge, MA, 162-169.

14. F.V. Jensen (1996), An Introduction to Bayesian Networks. Springer Verlag, New York.

15. S.L. Lauritzen and D.J. Spiegelhalter (1988), Local Computations with Probabilities on Graphical Structures and their Application to Expert Systems. Journal of the Royal Statistical Society, 50, No. 2.

16. Y. Lin and M.J. Druzdzel (1997), Computational Advantages of Relevance Reasoning in Bayesian Beliefs Networks. In Uncertainty in Artificial Intelligence: Proceedings of the Thirteenth Conference, 342-350. San Mateo, CA: Morgan Kaufmann.

17. R.E. Neapolitan (1990), Probabilistic Reasoning in Expert Systems: Theory and Algorithms. John Wiley & Sons, New York.

18. S.M. Olmsted (1983), On Representing and Solving Decision Problems. PhD. Thesis, Engineering-Economic Systems Dept., Standford Univ.

19. A. Onisko (2003). Probabilistic Causal Models in Medicine: Application to Diagnosis of Liver Disorders. Ph.D. Dissertation, Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw.

20. A. Onisko, M.J. Druzdzel and H. Wasyluk (2001), Learning Bayesian network parameters from small data sets: Application of Noisy-OR gates. International Journal of Approximate Reasoning, 27(2):165-182.

21. J. Pearl (1986), Fusion, Propagation, and Structuring in Belief Networks. Artificial Intelligence, 29(3), 241-288.

22. J. Pearl (1988), Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, Inc., San Mateo, CA.

23. R.D. Shachter (1988), Probabilistic Inference and Influence Diagrams. Operations Research, 36, 589-605.

24. R.D. Shachter and M.A. Peot (1990), Simulation Approaches to General Probabilistic Inference on Belief Networks. In M. Henrion and R.D. Shachter and L.N. Kanal and J.F. Lemmer (Eds.) Uncertainty in Artificial Intelligence, 5. North Holland. 221-231.

25. R.D. Shachter and M.A. Peot (1990), Simulation Approaches to General Probabilistic Inference on Belief Networks. In M. Henrion and R.D. Shachter and L.N. Kanal and J.F. Lemmer (Eds.) Uncertainty in Artificial Intelligence, 5. North Holland. 221-231.

26. R.D. Shachter and M.A. Peot (1992), Decision Making Using Probabilistic Inference Methods. in Proceedings of the Eighth Annual Conference on Uncertainty in Artificial Intelligence, 227-234. Stanford University, California.

27. C. Yuan and M.J. Druzdzel (2003), An importance sampling algorithm based on evidence pre-propagation. In Proceedings of the 19th Annual Conference on Uncertainty in Artificial Intelligence (UAI-03), pages 624-631, Morgan Kaufmann Publishers, Inc., San Francisco, CA.