General inference algorithm of Bayesian networks based on clique tree

2008 ◽  
Author(s):  
Haijun Li ◽  
Xiao Liu
Keyword(s):  
Bayesian Networks ◽  
Inference Algorithm ◽  
Clique Tree

Complex System Testability Analysis Based on Bayesian Networks under Small Sample

2014 ◽  
Vol 602-605 ◽  
pp. 1772-1777
Author(s):  
Xi Shan Zhang ◽  
Kao Li Huang ◽  
Peng Cheng Yan ◽  
Guang Yao Lian
Keyword(s):  
Complex System ◽  
Quantitative Analysis ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Prior Information ◽  
Learning Algorithm ◽  
Small Sample ◽  
Inference Algorithm ◽  
Network Learning ◽  
Testability Analysis

A lot of prior information in complex system test has been accumulated. To use the prior information for complex system testability quantitative analysis, a new complex system testability modeling and analyze method based on Bayesian network is presented. First, the complex system’s testability model is built using various kind of prior information by Bayesian network learning algorithm. Then, the way of assessing the testability of complex system is provided using the inference algorithm of Bayesian network. Finally, some proper examples are provided to prove the method’s validity.

scholarly journals Structured Bayesian Networks: From Inference to Learning with Routes

2019 ◽  
Vol 33 ◽  
pp. 7957-7965
Author(s):  
Yujia Shen ◽  
Anchal Goyanka ◽  
Adnan Darwiche ◽  
Arthur Choi
Keyword(s):  
Bayesian Networks ◽  
Graphical Models ◽  
Learning Algorithm ◽  
Simple Algorithm ◽  
Gps Data ◽  
Inference Algorithm ◽  
Exact Inference ◽  
Polynomial Size ◽  
Traditional Approaches ◽  
Domain Constraints

Structured Bayesian networks (SBNs) are a recently proposed class of probabilistic graphical models which integrate background knowledge in two forms: conditional independence constraints and Boolean domain constraints. In this paper, we propose the first exact inference algorithm for SBNs, based on compiling a given SBN to a Probabilistic Sentential Decision Diagram (PSDD). We further identify a tractable subclass of SBNs, which have PSDDs of polynomial size. These SBNs yield a tractable model of route distributions, whose structure can be learned from GPS data, using a simple algorithm that we propose. Empirically, we demonstrate the utility of our inference algorithm, showing that it can be an order-ofmagnitude more efficient than more traditional approaches to exact inference. We demonstrate the utility of our learning algorithm, showing that it can learn more accurate models and classifiers from GPS data.

REAL TIME INFERENCE IN BAYESIAN NETWORKS: AN ANYTIME APPROACH

2005 ◽  
Vol 14 (03) ◽  
pp. 477-489
Author(s):  
LAILA KHREISAT
Keyword(s):  
Bayesian Networks ◽  
Real Time ◽  
Computation Time ◽  
Resource Consumption ◽  
Inference Algorithm ◽  
Trade Off ◽  
Inference Algorithms ◽  
Output Quality ◽  
Probabilistic Inequalities ◽  
Limiting Case

One of the major challenges facing real time world applications that employ Bayesian networks, is the design and development of efficient inference algorithms. In this paper we present an approximate real time inference algorithm for Bayesian Networks. The algorithm is an anytime reasoning method based on probabilistic inequalities, capable of handling fully and partially quantified Bayesian networks. In our method the accuracy of the results improve gradually as computation time increases, providing a trade-off between resource consumption and output quality. The method is tractable in providing the initial answers, as well as complete in the limiting case.

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