Comparative Study of Parameter Learning Complexities of Enhanced Bayesian Networks

  • Monidipa DasEmail author
  • Soumya K. Ghosh
Part of the Studies in Computational Intelligence book series (SCI, volume 858)


The primary focus of this chapter is to provide a complete view of the computational complexity  in the parameter learning  of the enhanced Bayesian network models used in spatial time series prediction. The analysis is performed from both the perspectives of time and space  requirement. The chapter starts with a description of a common network configuration , 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, the parameter learning complexity for each of the BN models are estimated assuming the same configuration of the network. The comparative study at the end of the chapter shows that, even with the extended functionality, the parameter learning complexities in the enhanced BN models do not increase considerably compared to the standard BN model for spatial time series prediction.


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology KharagpurKharagpurIndia

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