Cattaneo, Marco E. G. V. (29. February 2008): Probabilistic-possibilistic belief networks. Department of Statistics: Technical Reports, No.32 |

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### Abstract

The interpretation of membership functions of fuzzy sets as statistical likelihood functions leads to a probabilistic-possibilistic hierarchical description of uncertain knowledge. The fundamental advantage of the resulting fuzzy probabilities with respect to imprecise probabilities is the ability of using all the information provided by the data. This paper studies the possibility of using fuzzy probabilities to describe the uncertain knowledge about the values of the nodes of belief networks.

Item Type: | Paper (Technical Report) |
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Form of publication: | Preprint |

Keywords: | likelihood function, hierarchical model, fuzzy probabilities, imprecise probabilities, updating, Bayesian networks, credal networks, conditional irrelevance, d-separation, possibilistic uncertainty |

Faculties: | Mathematics, Computer Science and Statistics > Statistics > Technical Reports |

URN: | urn:nbn:de:bvb:19-epub-4448-8 |

Language: | English |

ID Code: | 4448 |

Deposited On: | 17. Jun 2008 07:38 |

Last Modified: | 31. Aug 2018 11:21 |

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