Dynamic importance sampling computation in Bayesian networks

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 2711, Issue , 2003, Pages 137-148

  Moral, Serafín ^a   Salmerón, Antonio ^b  

^a UNIVERSITY OF GRANADA   (Spain)

^b UNIVERSITY OF ALMERÍA   (Spain)

Author keywords

Approximate algorithms; Bayesian networks; Importance sampling; Probability propagation

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; MONTE CARLO METHODS; POLYNOMIAL APPROXIMATION; PROBABILITY; SAMPLING; BAYESIAN NETWORKS;

APPROXIMATE ALGORITHMS; BAYESIAN NETWORKS; IMPORTANCE SAMPLING; PROBABILITY PROPAGATION; COMPUTATIONAL EFFORT; PROPAGATION ALGORITHM; SAMPLING DISTRIBUTION;

ARTIFICIAL INTELLIGENCE; IMPORTANCE SAMPLING;

EID: 8344266843     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-45062-7_11     Document Type: Conference Paper

References (23)

1. R.R. Bouckaert, E. Castillo, and J.M. Gutiérrez. A modified simulation scheme for inference in Bayesian networks. Int. Jnl. of Approximate Reasoning, 14:55-80, 1996.
2. A. Cano and S. Moral. Propagación exacta y aproximada con árboles de probabilidad. In Actas de la VII Conferencia de la Asociación Española para la Inteligencia Artificial, pages 635-644, 1997.
3. A. Cano, S. Moral, and A. Salmerón. Penniless propagation in join trees. Int. Jnl. of Intelligent Systems, 15:1027-1059, 2000.
4. A. Cano, S. Moral, and A. Salmerón. Lazy evaluation in penniless propagation over join trees. Networks, 39:175-185, 2002.
5. A. Cano, S. Moral, and A. Salmerón. Novel strategies to approximate probability trees in penniless propagation. Int. Jnl. of Intelligent Systems, 18:193-203, 2003.
6. J. Cheng, and M.J. Druzdzel. AIS-BN: An adaptive importance sampling algorithm for evidential reasoning in large Bayesian networks. Jnl of Artificial Intelligence Research, 13:155-188, 2000.
7. P. Dagum and M. Luby. Approximating probabilistic inference in Bayesian belief networks is NP-hard. Artificial Intelligence, 60:141-153, 1993.
8. P. Dagum and M. Luby. An optimal approximation algorithm for Bayesian inference. Artificial Intelligence, 93:1-27, 1997.
9. Elvira Consortium. Elvira: An environment for creating and using probabilistic graphical models. In J.A. Gámez and A. Salmerón, editors, Proceedings of the First European Workshop on Probabilistic Graphical Models, pages 222-230, 2002.
10. R. Fung and K.C. Chang. Weighting and integrating evidence for stochastic simulation in Bayesian networks. In M. Henrion, R.D. Shachter, L.N. Kanal, and J.F. Lemmer, editors, Uncertainty in Artificial Intelligence, volume 5, pages 209-220. North-Holland (Amsterdam), 1990.
11. L.D. Hernández, S. Moral, and A. Salmerón. A Monte Carlo algorithm for probabilistic propagation in belief networks based on importance sampling and stratified simulation techniques. Int. Jnl. of Approximate Reasoning, 18:53-91, 1998.
12. C.S. Jensen, A. Kong, and U. Kjærulff. Blocking Gibbs sampling in very large probabilistic expert systems. Int. Jnl. of Human-Computer Studies, 42:647-666, 1995.
13. U. Kjærulff. Reduction of computational complexity in Bayesian networks through removal of weak dependencies. In Proc. of the 10th Conference on Uncertainty in Artificial Intelligence, pages 374-382. Morgan Kaufmann, San Francisco, 1994.
14. S.L. Lauritzen and D.J. Spiegelhalter. Local computations with probabilities on graphical structures and their application to expert systems. Jnl. of the Royal Statistical Society, Series B, 50:157-224, 1988.
15. J. Pearl. Evidential reasoning using stochastic simulation of causal models. Artificial Intelligence, 32:247-257, 1987.
16. D. Poole. Probabilistic conflicts in a search algorithm for estimating posterior probabilities in bayesian networks. Artificial Intelligence, 88:69-100, 1996.
17. R.Y. Rubinstein. Simulation and the Monte Carlo Method. Wiley (New York), 1981.
18. A. Salmerón, A. Cano, and S. Moral. Importance sampling in Bayesian networks using probability trees. Comput. Statistics and Data Analysis, 34:387-413, 2000.
19. A. Salmeron and S. Moral. Importance sampling in Bayesian networks using antithetic variables. In S. Benferhat and P. Besnard, editors, Symbolic and Quantitative Approaches to Reasoning with Uncertainty, pages 168-179. Springer Verlag, 2001.
20. E. Santos and S.E. Shimony. Belief updating by enumerating high-probability independence-based assignments. In Proceedings of the 10th Conference on Uncertainty in Artificial Intelligence, pages 506-513, 1994.
21. E. Santos, S.E. Shimony, and E. Williams. Hybrid algorithms for approximate belief updating in Bayes nets. Int. Jnl. of Approximate Reasoning, 17:191-216, 1997.
22. R.D. Shachter and M.A. Peot. Simulation approaches to general probabilistic inference on belief networks. In M. Henrion, R.D. Shachter, L.N. Kanal, and J.F. Lemmer, editors, Uncertainty in Artificial Intelligence, volume 5, pages 221-231. North Holland (Amsterdam), 1990.
23. N.L. Zhang and D. Poole. Exploiting causal independence in Bayesian network inference. Jnl. of Artificial Intelligence Research, 5:301-328, 1996.