L. D. Hernandez, S. Moral, and A. Salmeron. A Monte Carlo algorithm for probabilistic propagation in belief networks based on importance sampling and stratified simulation techniques. International Journal of Approximate Reasoning, 18:53--91, 1998. Home/Search Document Not in Database Summary Related Articles Check |
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A Survey of Algorithms for Real-Time Bayesian Network Inference - Guo, Hsu (2002) (2 citations) (Correct)
....function infrequently using the scores generated in the algorithm. HIS computes its importance function by performing a modified version of the singly connected evidence propagation algorithm. Other implementations of importance sampling include Cano s and Hernandez importance sampling algorithms [CHM96, HMA98]. The experimental result reported shows that they all perform better than likelihood weighting. Bounded variance and AA algorithms are variants of likelihood weighting described by Dagum and Luby [DKLS95, DL97] They are based on the LW algorithm and the Stopping Rule Theorem. They work better ....
L. D. Hernandez, S. Moral, and S. Antonio. A Monte Carlo algorithm for probabilistic propagation in belief networks based on importance sampling and stratified simulation techniques. International Journal of Approximate Reasoning, 18:53-91, 1998.
AIS-BN: An Adaptive Importance Sampling Algorithm for.. - Cheng, Druzdzel (2000) (10 citations) (Correct)
....Theorem (Dagum et al. 1995) Cano et al. 1996) proposed another importance sampling algorithm that performed somewhat better than LW in cases with extreme probability distributions, but, as the authors state, in general cases it produced similar results to the likelihood weighting algorithm. Hernandez et al. 1998) also applied importance sampling and reported a moderate improvement on likelihood weighting. 2.4 Practical Performance of the Existing Sampling Algorithms The largest network that has been tested using sampling algorithms is QMR DT (Quick Medical Reference Decision Theoretic) Shwe et al. ....
Hernandez, L. D., Moral, S., & Antonio, S. (1998). A Monte Carlo algorithm for probabilistic propagation in belief networks based on importance sampling and stratied simulation techniques. International Journal of Approximate Reasoning, 18, 53-91.
An Importance Sampling Algorithm Based on Evidence.. - Yuan, Druzdzel (Correct)
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L. D. Hernandez, S. Moral, and A. Salmeron. A Monte Carlo algorithm for probabilistic propagation in belief networks based on importance sampling and stratified simulation techniques. International Journal of Approximate Reasoning, 18:53--91, 1998.
A Bayesian System for Integration of Algorithms for Real-Time.. - Guo (2002) (Correct)
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L. D. Hernandez, S. Moral, and S. Antonio. A Monte Carlo algorithm for probabilistic propagation in belief networks based on importance sampling and stratified simulation techniques. International Journal of Approximate Reasoning, 18:53-91, 1998.
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