Bayesian network inference using marginal trees

Butz, Cory J.; Oliveira, Jhonatan S.; Madsen, Anders L.

doi:10.1016/j.ijar.2015.07.006

Cited by 5 publications

(1 citation statement)

References 19 publications

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“…The inference algorithm of the Bayesian network (BN) based on graph theory and probability theory can be used for bidirectional inference [6], namely, it can reason reliability of the system from root nodes to leaf nodes and weak linkage of the system from leaf nodes to root nodes. Therefore, it is widely used in fields such as cancer prediction [7], travel planning [8], classification of traditional Chinese medicine [9], and fault diagnosis [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Fault Inference of Electronic Equipment Based on Multi-State Fuzzy Bayesian Network

et al. 2019

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The aim of this study is to extend the directive function of fault inference in test and diagnosis system for electronic equipment. There are many problems, such as presence of various types of uncertain information in test set of electronic equipment, frequent degenerative faults, complex relationships of modules, multiple fault modes, existence of fuzzy interval in fault state, and interaction of each module. In view of these problems, the total membership degree of faults is commonly synthesized based on weights of multiple test indicators and normal membership degree of a single indicator. On this basis, this study builds the model for inferring fault states of leaf and root nodes based on multi-state triangular fuzzy Bayesian network (BN). Finally, this research carried out feasibility analysis on fault inference of a super-heterodyne receiver, thus verifying the efficiency and applicability of the method proposed in the study.

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Section: Introductionmentioning

confidence: 99%

Fault Inference of Electronic Equipment Based on Multi-State Fuzzy Bayesian Network

et al. 2019

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Identification of significant imaging features for sensing oocyte viability

Chen

Liu

Zuo

et al. 2022

Microscopy Res & Technique

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The evaluation of oocyte viability in the laboratory is limited to the morphological assessment by naked eyes, but the realization that most normal-appearing oocytes may conceal abnormalities prompts the search for automated approaches that can detect the abnormalities imperceptible to naked eyes. In this study, we developed an image processing pipeline applicable to bright-field microscope images to quantify the causal relationship between the quantitative imaging features and the developmental potential of oocytes. We acquired 19 imaging features of approximately 700 oocytes and determined two imaging subtypes, namely viable and nonviable subtypes that correlated closely with a viability fluorescence indicator and cleavage rates. The causal relationship between these imaging features and oocyte viability was derived from a viability-oriented Bayesian network that was developed based on the Bayesian information criterion and Tabu search. Our experimental results revealed that entropy with mean Gray Level Co-Occurrence Matrix energy describing the uniformity and texture roughness of cytoplasm were salient features for the automated selection of promising oocytes that exhibited excellent developmental potential.

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