Advanced Bayesian Network Models with Fuzzy Extension

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


Fuzzy Bayesian networks (FBNs) are the variant of standard/classical Bayesian networks (BNs), which have intrinsic capability of handling ambiguity due to lack of expert knowledge and eventually reduces the epistemic uncertainty  when used as computational models. Of late, FBNs have gained substantial research interest to be applied for time series prediction in both non-spatial and spatial domains . This chapter discusses a number of fuzzy BN models that have recently been proposed in literature. The central attention is paid on how the discrete Bayesian analysis in the previously discussed enhanced BN models can be further improved through incorporated fuzziness so as to make them more realistic for dealing with various contexts of spatial time series  prediction. A comparative study, at the end of the chapter, demonstrates superiority of the fuzzified enhanced BN models, compared to those having no incorporated fuzziness.


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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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