In recent years there has been a growing interest in Bayesian Network learning from uncertain data. While many researchers focus on Bayesian Network learning from data with tuple uncertainty, Bayesian Network structure learning from data with attribute uncertainty gets little attention. In this paper we make a clear definition of attribute uncertain data and Bayesian Network Learning problem fr...

This paper addresses the problem of learning Bayesian network structures from data based on score functions that are decomposable. It describes properties that strongly reduce the time and memory costs of many known methods without losing global optimality guarantees. These properties are derived for different score criteria such as Minimum Description Length (or Bayesian Information Criterion)...

Finding the optimistic triangulation in Bayesian network, is NP hard. Bayesian Optimization Algorithm is a new kind of evolutionary algorithm estimation of distribution algorithms (EDAs). An improved BOA is proposed to get approximate optimistic triangulation in this paper. We carry out four EDAs including our method, on four standard Bayesian networks. Comparing with other Estimation of distri...

| Given a set of samples of an unknown probability distribution, we study the problem of constructing a good approxi-mative Bayesian network model of the probability distribution in question. This task can be viewed as a search problem, where the goal is to nd a maximal probability network model, given the data. In this work, we do not make an attempt to learn arbitrarily complex multi-connecte...

We describe a technique for improving the classification of fragmented cues for cracks. Evidence propagation on Bayesian networks represent search within the context of each cue. The algorithm was applied to a data-set of cracks, and results demonstrate that contextual classification of the cues leads to significantly improved error rates.

Context-specific independence is useful as it can lead to improved inference in Bayesian networks. In this paper, we present a method for detecting this kind of independence from data and emphasize why such an algorithm is needed.

Mobile learning technologies have the potential to revolutionize distance education by bringing the concept of anytime and anywhere to reality. However, the development of mobile learning is hampered by various technological and access related problems, including the difficulty in implementing adaptivity. In this paper, we use the Bayesian networks to determine mobile learner’s styles exploring...

We address the problem of identifying dynamic sequential plans in the framework of causal Bayesian networks, and show that the problem is reduced to identifying causal effects, for which there are complete identification algorithms available in the literature.

Learning the structure of a Bayesian network from data is a difficult problem, as its associated search space is superexponentially large. As a consequence, researchers have studied learning Bayesian networks with a fixed structure, notably naive Bayesian networks and tree-augmented Bayesian networks, which involves no search at all. There is substantial evidence in the literature that the perf...