Probabilistic inference and maximum a posteriori (MAP) explanation are two important and related problems on Bayesian belief networks. Both problems are known to be NP-hard for both approximation and exact solution. In 1997, Dagum and Luby showed that efficiently approximating probabilistic inferenc
Importance sampling algorithms for the propagation of probabilities in belief networks
✍ Scribed by Jose E. Cano; Luis D. Hernández; Serafín Moral
- Publisher
- Elsevier Science
- Year
- 1996
- Tongue
- English
- Weight
- 769 KB
- Volume
- 15
- Category
- Article
- ISSN
- 0888-613X
No coin nor oath required. For personal study only.
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