We analyse a quantum-like Bayesian Network that puts together cause/effect relationships and semantic similarities between events. These semantic similarities constitute acausal connections according to the Synchronicity principle and provide new relationships to quantum like probabilistic graphical models. As a consequence, beliefs (or any other event) can be represented in vector spaces, in which quantum parameters are determined by the similarities that these vectors share between them. Events attached by a semanticmeaning do not need to have an explanation in terms of cause and effect.
CITATION STYLE
Moreira, C., & Wichert, A. (2016). The relation between acausality and interference in quantum-like bayesian networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9535, pp. 129–141). Springer Verlag. https://doi.org/10.1007/978-3-319-28675-4_10
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