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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Semantic Bayesian Network,of 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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Comparative Study of Parameter Learning Complexities of Enhanced Bayesian Networks,formed from both the perspectives of .?and .??requirement. The chapter starts with a description of a common .?, specifying the total number of nodes/variables, maximum number of parents for any node in the network, maximum domain size of the variables, total number of spatial locations etc. Later,

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,Spatial Time Series Prediction Using Advanced BN Models—An Application Perspective,ls from the perspective of various applications of .?prediction. Eight different application domains including medical imaging, .?, transportation, bio-informatics, homeland security, environment/ecology, finance/economy, etc. have been considered for this purpose. Later, the chapter also discusses