Efficient inference for mixed Bayesian networks
Document Type
Conference Proceeding
Publication Date
1-1-2002
Abstract
A Bayesian network is a compact representation for probabilistic models and inference. They have been used successfully for multisensor fusion and situation assessment. It is well known that, in general, the inference algorithms to compute the exact posterior probability of the target state are either computationally infeasible for dense networks or impossible for mixed discrete continuous networks. In those cases, one approach is to compute the approximate results using simulation methods. This paper proposes efficient inference methods for those cases. The goal is not to compute the exact or approximate posterior probability of the target state, but to identify the top (most likely) ones in an efficient manner. The approach is to use intelligent simulation techniques where previous samples will be used to guide the future sampling strategy. By focusing the sampling on the "important" space, we are able to sort out the top candidates quickly. Simulation results are included to demonstrate the performances of the algorithms. © 2002 Int. Soc. of Information Fusion.
Publication Title
Proceedings of the 5th International Conference on Information Fusion, FUSION 2002
Recommended Citation
Chang, K.,
&
Tian, Z.
(2002).
Efficient inference for mixed Bayesian networks.
Proceedings of the 5th International Conference on Information Fusion, FUSION 2002,
1, 527-534.
http://doi.org/10.1109/ICIF.2002.1021199
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/10580