Bayesian Networks (BNs)
Comparing HMMs and Bayesian Networks for Surface Realisation
... and Bayesian Networks (BNs) which both have been suggested as generation spaces—spaces of surface form variants for a semantic concept— within joint NLG systems (Dethlefs and Cuay´ahuitl, 2011a; Dethlefs ...
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Constructing Bayesian Networks Automatically using Ontologies
... a Bayesian Network is complex and ...for Bayesian Networks has be- come a hot topic in the data mining ...a Bayesian Network in order to correctly specify relations between the variables in ...
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A primer on learning in Bayesian networks for computational biology
... Bayesian networks (BNs) provide a neat and compact representation for expressing joint probability distributions (JPDs) and for ...cellular networks [1], modelling protein signalling pathways [2], ...
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Learning Diverse Bayesian Networks
... Bayesian networks (BN) (Pearl 1988) are graphical models that represent probabilistic dependencies between random ...optimal Bayesian networks, including dy- namic programming (Koivisto and ...
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The Representational Power of Discrete Bayesian Networks
... of Bayesian networks by proposing and proving a representational upper bound of ...a Bayesian network of order m cannot represent a target function of order m, if each of the nodes that forms the ...
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A Scoring Function for Learning Bayesian Networks based on Mutual Information and Conditional Independence Tests
... learning Bayesian networks from data consists in finding the BN that (according to certain criterion) best fits the available ...As Bayesian networks have two different components (the ...
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Strong Limit Theorems for the Bayesian Scoring Criterion in Bayesian Networks
... Bayesian networks are graphical structures which characterize probabilistic relationships among variables of interest and serve as a ground model for doing probabilistic inference in large systems of ...
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Learning Bounded Treewidth Bayesian Networks
... the greedy junction tree approach (dashed red circles) are superior to the aggressive baseline (dotted black). As one might expect, the aggressive baseline achieves a higher BIC score on training data (not shown), but ...
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Analyzing Student Process Data in Game-Based Assessments with Bayesian Knowledge Tracing and Dynamic Bayesian Networks
... Dynamic Bayesian Networks and Bayesian Knowledge Tracing to estimate and update student mastery of knowledge and skills for game- and simulation-based ...Dynamic Bayesian Networks and ...
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Local Propagation in Conditional Gaussian Bayesian Networks
... discrete Bayesian networks and purely continuous multivariate Gaussian Bayesian net- works the junction or elimination trees described above may be used for exact propagation algo- rithms, with any ...
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Comparison of the Bayesian Networks using Microarray Data
... 4. S. Acid, l. M. d. Campos, J. M. Fernández- luna, S. Rodríguez, J. M. Rodríguez, and J. l. Salcedo, “A comparison of learning algorithms for Bayesian networks: a case study based on data from an emergency ...
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Large incomplete sample robustness in Bayesian networks
... Under local DeRobertis (LDR) separation measures, the posterior distances be- tween two densities is the same as between the prior densities. Like Kullback - Leibler separation they also are additive under factorization. ...
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Inner Product Spaces for Bayesian Networks
... Bayesian networks have a long history in ...(1984). Bayesian networks are much different from kernel-based learning systems and offer some complementary ...models, Bayesian ...
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Asymptotic Model Selection for Naive Bayesian Networks
... for Bayesian model selection among Bayesian networks with hidden ...naive Bayesian model; it complements the main result presented by Theorem ...
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Importance Sampling for Continuous Time Bayesian Networks
... The line numbers follow those given in the forward sampling algorithm with new or changed lines marked with an asterisk. Time(X) might be set to the end of an interval of evidence which is not a transition time but ...
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Learning Non-Stationary Dynamic Bayesian Networks
... dynamic Bayesian network structures provides a principled mechanism for identifying conditional dependencies in time-series ...dynamic Bayesian network, in which the conditional dependence structure of the ...
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Exact Bayesian Structure Discovery in Bayesian Networks
... Exact algorithms for structure learning have been presented for very restricted classes of Bayesian networks only. The algorithm by Cooper and Herskovits (1992) is polynomial in the number of vari- ables, ...
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Construction of gene regulatory networks using biclustering and bayesian networks
... biclustering networks via the gold network retrieved by BioNetBuilder [33] and the Friedman network ...the networks generated from different bicluster algorithms nor the ALL network perform ...
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Bayesian Process Networks: An approach to systemic process risk analysis by mapping process models onto Bayesian networks
... specified under the condition that the preceding XOR or OR variable has established a distribution of the control flow not having considered this event. Under this condition, neither the reference nor the risk state of ...
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Model Averaging for Prediction with Discrete Bayesian Networks
... classes of interest. A probabilistic model accomplishes this goal by calculating the posterior proba- bility, P(C | F), of the class given the features. One of the simplest probabilistic classifiers for this task is the ...
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