Titlebook: Enhanced Bayesian Network Models for Spatial Time Series Prediction; Recent Research Tren Monidipa Das,Soumya K. Ghosh Book 2020 Springer N

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https://doi.org/10.1007/978-3-319-65256-6mes large, containing several nodes and edges, the .?of Bayesian network (BN) analysis increases at a large extent. Now, in many cases of .?prediction, it is necessary to take into account the influences of variables from large number of spatially distributed locations, which becomes almost intracta

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Fabien Escalona,Daniel Keith,Luke Marchof available training dataset. A proper learning of the network needs large amount of observed data be available during the training procedure. Otherwise, it may result in strongly biased inference due to .. The recent research indicates that a prior knowledge about the respective . ?may help in red

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,Spatial Time Series Prediction Using Advanced BN Models—An Application Perspective,on the synergism of enhanced BN models to handle more complex ST prediction scenarios in real life. We anticipate that the chapter will help researchers to find out several interesting research issues yet to be resolved and will also encourage them to further explore the intrinsic power of BNs to tackle the same.

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parameter learning and .. We also cover the basic concepts of various categories of .?, including dynamic Bayesian network, fuzzy Bayesian network, spatial Bayesian network, semantic Bayesian network etc. Further, we discuss on the potentials of BN in modeling the inter-variable dependencies while analyzing ..

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