Emerging smart environment infrastructures provide users with a large number of interconnected assisting appliances. Inferring the intention of a team within such an area becomes a central challenge, especially if multiple user are observed by noisy heterogenous sensors. We propose a team task model based on hierarchical Dynamic Bayesian Network (DBN) for inferring the current task and activity...

Different probabilistic models for classification and prediction problems are anlyzed in this article studying their behaviour and capability in data classification. To show the capability of Bayesian Networks to deal with classification problems four types of Bayesian Networks are introduced, a General Bayesian Network, the Naive Bayes, a Bayesian Network Augmented Naive Bayes and the Tree Aug...

A large number of data is needed by the computation of the objective Bayesian network, but the data is hard to get in actual computation. The calculation method of Bayesian network was improved in this paper, and the fuzzy-precise Bayesian network was obtained. Then, the fuzzy-precise Bayesian network was used to reason Bayesian network model when the data is limited. The security of passengers...

This paper discusses issues related to Bayesian network model learning for unbalanced binary classification tasks. In general, the primary focus of current research on Bayesian network learning systems (e.g., K2 and its variants) is on the creation of the Bayesian network structure that fits the database best. It turns out that when applied with a specific purpose in mind, such as classificatio...

A Bayesian Network is a construct that is used to model a given joint probability distribution. In order to assess the quality of an inference, or to choose between competing networks modelling the same data, we need methods to estimate the accuracy of a Bayesian network. Although the accuracy of a Bayesian network can be easily defined in theory, it is rarely possible to compute it in practice...