Optimizing the Calculation of Conditional Probability Tables in Hybrid Bayesian Networks Using Binary Factorization

2012 ◽  
Vol 24 (7) ◽  
pp. 1306-1312 ◽  
Author(s):  
M. Neil ◽  
Xiaoli Chen ◽  
N. Fenton
Keyword(s):  
Bayesian Networks ◽  
Conditional Probability ◽  
Hybrid Bayesian Networks

Application of Bayesian for the Situation Assessment of Sea-Battlefield

2013 ◽  
Vol 346 ◽  
pp. 135-139 ◽  
Author(s):  
Yong Tao Yu ◽  
Ying Ding ◽  
Zheng Xi Ding
Keyword(s):  
Decision Support ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Conditional Probability ◽  
Assessment Model ◽  
Situation Assessment ◽  
Network Characteristics ◽  
Specific Assessment

The sea-battlefield situation is dynamic and how efficient sea-battlefield situation assessment is a major problem facing operational decision support. According to research based on Bayesian networks Sea-battlefield situation assessment, first constructed sea-battlefield situation assessment Bayesian network; followed by specific assessment objectives, to simplify creating sub Bayesian assessment model; once again based on Bayesian network characteristics to determine each node probability formula; finally, according to the formula for solving the edge of the probability and the conditional probability of each node, sea-battlefield situation assessment.

scholarly journals Gaussian Mixture Reduction for Time-Constrained Approximate Inference in Hybrid Bayesian Networks

2019 ◽  
Vol 9 (10) ◽  
pp. 2055 ◽  
Author(s):  
Cheol Young Park ◽  
Kathryn Blackmond Laskey ◽  
Paulo C. G. Costa ◽  
Shou Matsumoto
Keyword(s):  
Bayesian Networks ◽  
Message Passing ◽  
Random Variables ◽  
Computation Time ◽  
Gaussian Mixture ◽  
Image Understanding ◽  
Approximate Inference ◽  
Continuous Variables ◽  
Hybrid Bayesian Networks ◽  
Trade Off

Hybrid Bayesian Networks (HBNs), which contain both discrete and continuous variables, arise naturally in many application areas (e.g., image understanding, data fusion, medical diagnosis, fraud detection). This paper concerns inference in an important subclass of HBNs, the conditional Gaussian (CG) networks, in which all continuous random variables have Gaussian distributions and all children of continuous random variables must be continuous. Inference in CG networks can be NP-hard even for special-case structures, such as poly-trees, where inference in discrete Bayesian networks can be performed in polynomial time. Therefore, approximate inference is required. In approximate inference, it is often necessary to trade off accuracy against solution time. This paper presents an extension to the Hybrid Message Passing inference algorithm for general CG networks and an algorithm for optimizing its accuracy given a bound on computation time. The extended algorithm uses Gaussian mixture reduction to prevent an exponential increase in the number of Gaussian mixture components. The trade-off algorithm performs pre-processing to find optimal run-time settings for the extended algorithm. Experimental results for four CG networks compare performance of the extended algorithm with existing algorithms and show the optimal settings for these CG networks.

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