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Proceedings Paper

Comparing probabilistic inference for mixed Bayesian networks
Author(s): Kuo Chu Chang; Wei Sun
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Paper Abstract

Bayesian Networks are graphical representation of dependence relationships between domain variables. They have been applied in many areas due to their powerful probabilistic inference such as data fusion, target recognition, and medical diagnosis, etc. There exists a number of inference algorithms that have different tradeoffs in computational efficiency, accuracy, and applicable network topologies. It is well known that, in general, the exact inference algorithms are either computationally infeasible for dense networks or impossible for mixed discrete-continuous networks. However, in practice, mixed Bayesian Networks are commonly used for various applications. In this paper, we compare and analyze the trade-offs for several inference approaches. They include the exact Junction Tree algorithm for linear Gaussian networks, the exact algorithm for discretized networks, and the stochastic simulation methods. We also propose an almost instant-time algorithm (AIA) by pre-compiling the approximate likelihood tables. Preliminary experimental results show promising performance.

Paper Details

Date Published: 25 August 2003
PDF: 8 pages
Proc. SPIE 5096, Signal Processing, Sensor Fusion, and Target Recognition XII, (25 August 2003); doi: 10.1117/12.486863
Show Author Affiliations
Kuo Chu Chang, George Mason Univ. (United States)
Wei Sun, George Mason Univ. (United States)

Published in SPIE Proceedings Vol. 5096:
Signal Processing, Sensor Fusion, and Target Recognition XII
Ivan Kadar, Editor(s)

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