Probabilistic conditional independence: a similarity-based measure and its application to causal network learning
A new definition for similarity between possibility distributions is introduced and discussed as a basis for detecting dependence between variables by measuring the similarity degree of their respective distributions. This new definition is used to detect conditional independence relations in possib...
| Autores: | , , |
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| Tipo de documento: | relatório científico |
| Data de publicação: | 1996 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositório: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglês |
| OAI Identifier: | oai:upcommons.upc.edu:2117/84564 |
| Acesso em linha: | https://hdl.handle.net/2117/84564 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Similarity Possibility distributions POSSCAUSE Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
| Resumo: | A new definition for similarity between possibility distributions is introduced and discussed as a basis for detecting dependence between variables by measuring the similarity degree of their respective distributions. This new definition is used to detect conditional independence relations in possibility distributions derived from data. This is the basis for a new hybrid algorithm for recovering possibilistic causal networks. The algorithm POSSCAUSE is presented and its applications discussed and compared with analogous developments in possibilistic and probabilistic causal networks learning. |
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