bayesian networks
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Learning Tractable Bayesian Networks
... 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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Dynamic bayesian networks for rainfall forecasting
... 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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Usefulness of Bayesian networks in epidemiological studies
... 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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Ranking features on psychological dynamics of cooperative team work through Bayesian networks
... 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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Learning Bayesian networks from data by the incremental compilation of new network polynomials
... 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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Learning Bayesian Networks for Student Modeling
... 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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Bayesian networks for probabilistic weather prediction
... 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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Fault Diagnosis of Hybrid Systems with Dynamic Bayesian Networks and Hybrid Possible Conficts
... 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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Learning low inference complexity Bayesian networks
... 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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Regression models with MoPs Bayesian networks
... 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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Tree-structured Bayesian networks for wrapped Cauchy directional distributions
... (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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Multi-facet determination for clustering with Bayesian networks
... 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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Using Bayesian networks to improve knowledge assessment
... 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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Decision boundary for discrete Bayesian network classifiers
... 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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Bayesian modelling of fault diagnosis in mobile communication networks
... 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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The design of a Bayesian Network for mobility management in Wireless Sensor Networks
... 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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Contributions to Bayesian network learning with applications to neuroscience
... 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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Multidimensional classifiers for neuroanatomical data
... 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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