No comprehensive review of Bayesian networks (BNs) in healthcare has been published the past, making it difficult to organize research contributions present and identify challenges neglected areas that need be addressed future. This unique novel scoping BNs provides an analytical framework for comprehensively characterizing domain its current state. A literature search health informatics databa...

The inference of gene regulatory networks (GRNs) from expression data can mine the direct regulations among genes and gain deep insights into biological processes at a network level. During past decades, numerous computational approaches have been introduced for inferring the GRNs. However, many of them still suffer from various problems, e.g., Bayesian network (BN) methods cannot handle large-...

Recent work in Bayesian classifiers has shown that a better and more flexible representation of domain knowledge results in better classification accuracy. In previous work [1], we have introduced a new type of Bayesian classifier called Case-Based Bayesian Network (CBBN) classifiers. We have shown that CBBNs can capture finer levels of semantics than possible in traditional Bayesian Networks (...

Bayesian network (BN) is a directed acyclic graph encoding probabilistic independence statements between variables. BN with decision attribute as a root can be applied to classification of new cases, by synthesis of conditional probabilities propagated along the edges. We consider approximate BNs, which almost keep entropy of a decision table. They have usually less edges than classical BNs. Th...

Automatic generation of Bayesian network (BNs) structures (directed acyclic graphs) is an important step in experimental study of algorithms for inference in BNs and algorithms for learning BNs from data. Previously known simulation algorithms do not guarantee connectedness of generated structures or even successful genearation according to a user specification. We propose a simple, efficient a...

Leakage is one of the most important reasons for failure hydraulic systems. The accurate positioning leakage great significance to ensure safe and reliable operation For early stage leakage, pressure circuit does not change obviously therefore cannot be monitored by sensors. Meanwhile, changes frequently due influence load state switch, which further reduces accuracy localization. In work, a no...

Most learning algorithms assume that a data set is given initially. We address the common situation where data is not available initially, but can be obtained, at a cost. We focus on learning Bayesian belief networks (BNs) over discrete variables. As such BNs are models of probabilistic distributions, we consider the “generative” challenge of learning the parameters for a fixed structure, that ...

Bayesian Networks (BNs) have received significant attention in various academic and industrial applications, such as modeling knowledge in image processing, engineering, medicine and bio-informatics. Preserving the privacy of sensitive data, owned by different parties, is often a critical issue. However, in many practical applications, BNs must train from data that gradually becomes available a...

Bayesian networks (BNs) are useful for coding conditional independence statements between a given set of measurement variables. On the other hand, event trees (ETs) are convenient for representing asymmetric structure and how situations unfold. In this paper we report the development of a new graphical framework for discrete probability models called the Chain Event Graph (CEG). The class of CE...

The level of seriousness and sophistication of recent cyberattacks has risen dramatically over the past decade. This brings great challenges for network protection and the automatic security management. Quick and exact localization of intruder by an efficient intrusion detection system (IDS) will be great helpful to network manager. In this paper, Bayesian networks (BNs) are proposed to model t...