... practice. **Bayesian** **networks** (BNs) address this problem applying probability theory, that is long estab- lished, using probabilities to indicate different degrees of ...

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... how **Bayesian** **networks** (BNs) and dynamical **Bayesian** net- works (DBNs) offer a sound and practical methodology for building probabilistic models from data ...dynamic **networks** for representing ...

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... Introduction: **Bayesian** **networks** are a form of statistical modelling, which has been widely used in fields like clinical decision, systems biology, human immunodeficiency virus (HIV) and influenza research, ...

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... **Bayesian** **networks** (BNs) [14–18], well suited to reason with uncertain domain knowledge, can be applied to aid teams by providing cooperative and collaborative work characterization ...

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... Probabilistic graphical models are a huge research field in artificial intelligence nowa- days. The scope of this work is the study of directed graphical models for the representation of discrete distributions. Two of ...

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... using **Bayesian** **Networks** (BN) in the student modelling ...a **Bayesian** student model, it is necessary to define the structure (nodes and links) and the ...two **Bayesian** Student Models, whose ...

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... introduce **Bayesian** **Networks** (BNs) in this frame- work to model the spatial and temporal dependencies among the dif- ferent stations using a directed acyclic ...

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... Dynamic **Bayesian** **Networks** are a probabilistic temporal model representation of a dynamic system. Basi- cally, a DBN can be deﬁned as a two-slice **Bayesian** Network (BN). There are two assumptions ...

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... **Bayesian** **networks** (BNs) are very powerful tools for concisely modelling proba- bility distributions of a set of random ...the **networks**, learning sometimes spurius relations among variables and ...

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... of **Bayesian** network for continuous variables, where densities and con- ditional densities are estimated with B-spline ...Keywords: **Bayesian** **networks**, regression, MoP, conditional density estimation, ...

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... (P14) rat hind limb somatosensory (S1HL) neocortex recently published in [13]. Dendritic bifurcation angles are an important part of the geometry of pyramidal cell basal arbors. The comprehension and capability of ...

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... Real world applications of sectors like industry, healthcare or finance usually generate data of high complexity that can be interpreted from different viewpoints. When clustering this type of data, a single set of ...

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... The student model chosen to be integrated into the computerized testing tools of the PMatE was a previously generic BSM developed by Mill´an and P´erez de la Cruz (2002). To refer to this model, we will use the acronym ...

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... different **Bayesian** **networks** considered are not in general ...given **Bayesian** network classifier, and we will be able to compute the gain in expressivity from simple to more complicated **Bayesian** ...

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... After some months, Nokia decided to start a project on automatic troubleshooting in cellular **networks**. Because of my previous experience, this project fitted perfectly with my interests and, at the same time, gave ...

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... This paper describes how the **Bayesian** network approach can make explicit the structure and parameters of different movement contexts of a mobile WSN. It also shows how these movement contexts play an important ...

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... populations of angular data belonging to two different class labels. More recently, SenGupta and Ugwuowo [450] proposed a likelihood ratio test based on a bootstrapping approach to classify angular and linear data. These ...

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... a **Bayesian** network of this number of variables is quite large, other learning strategies may be considered for the same dataset as other local optima, perhaps with the capability to improve all scores, are ...

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