How heavy should the tails be?

Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005 - Recent Advances in Artifical Intelligence

Volumn , Issue , 2005, Pages 799-804

  Yuan, Changhe ^a   Druzdzel, Marek J ^a  

^a UNIVERSITY OF PITTSBURGH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; HEAVY TAILS; TARGET DENSITY;

COMPUTER NETWORKS; HEURISTIC METHODS; SAMPLING; THEOREM PROVING;

ALGORITHMS;

EID: 32844469539     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. Cheng, J., and Druzdzel, M. J. 2000. BN-AIS: An adaptive importance sampling algorithm for evidential reasoning in large Bayesian networks. Journal of Artificial Intelligence Research 13:155-188.
2. Cooper, G. F. 1990. The computational complexity of probabilistic inference using Bayesian belief networks. Artificial Intelligence 42(2-3):393-405.
3. Dagum, P., and Luby, M. 1993. Approximating probabilistic inference in Bayesian belief networks is NP-hard. Artificial Intelligence 60(1):141-153.
4. Druzdzel, M. J. 1994. Some properties of joint probability distributions. In Proceedings of the 10th Conference on Uncertainty in Artificial Intelligence (UAI-94), 187-194.
5. Fung, R., and Chang, K.-C. 1989. Weighing and integrating evidence for stochastic simulation in Bayesian networks. In Henrion, M.; Shachter, R.; Kanal, L.; and Lemmer, J., eds., Uncertainty in Artificial Intelligence 5, 209-219. New York, N. Y.: Elsevier Science Publishing Company, Inc.
6. Fung, R., and del Favero, B. 1994. Backward simulation in Bayesian networks. In Proceedings of the Tenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-94), 227-234. San Mateo, CA: Morgan Kaufmann Publishers, Inc.
7. Geweke, J. 1989. Bayesian inference in econometric models using Monte Carlo integration. Econometrica 57(6):1317-1339.
8. Henrion, M. 1988. Propagating uncertainty in Bayesian networks by probalistic logic sampling. In Uncertainty in Artificial Intelligence 2, 149-163. New York, N.Y.: Elsevier Science Publishing Company, Inc.
9. Hernandez, L. D.; Moral, S.; and Salmeron, A. 1998. A Monte Carlo algorithm for probabilistic propagation in belief networks based on importance sampling and stratifi ed simulation techniques. International Journal of Approximate Reasoning 18:53-91.
10. Koopman, S. J., and Shephard, N. 2002. Testing the assumptions behind the use of importance sampling. Technical report 2002-w17, Economics Group, Nuffi eld College, University of Oxford.
11. Liu, J. S. 2001. Monte Carlo Strategies in Scientific Computing. New York: Springer-Verlag.
12. MacKay, D. 1998. Introduction to Monte Carlo methods. Cambridge, Massachusetts: The MIT Press.
13. Moral, S., and Salmeron, A. 2003. Dynamic importance sampling computation in Bayesian networks. In Proceedings of Seventh European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU-03), 137-148.
14. Neal, R. M. 1998. Annealed importance sampling. Technical report no. 9805, Dept. of Statistics, University of Toronto.
15. Ortiz, L., and Kaelbling, L. 2000. Adaptive importance sampling for estimation in structured domains. In Proceedings of the I6th Annual Conference on Uncertainty in Artificial Intelligence (UAI-00), 446-454.
16. Rubinstein, R. Y. 1981. Simulation and the Monte Carlo Method. John Wiley & Sons.
17. Shachter, R. D., and Peot, M. A. 1989. Simulation approaches to general probabilistic inference on belief networks. In Henrion, M.; Shachter, R.; Kanal, L.; and Lemmer, J., eds., Uncertainty in Artificial Intelligence 5, 221-231. New York, N. Y: Elsevier Science Publishing Company, Inc.
18. Smith, R. L. 1987. Estimating tails of probability distributions. Annals of Statistics 15:1174-1207.
19. Yuan, C., and Druzdzel, M. J. 2004. A comparison of the effectiveness of two heuristics for importance sampling. In Proceedings of the Second European Workshop on Probabilistic Graphical Models (PGM '04), 225-232.
20. Yuan, C., and Druzdzel, M. J. 2005. Importance sampling algorithms for Bayesian networks: Principles and performance. To appear in Mathematical and Computer Modelling.