The Relation Between Acausality and Interference in Quantum-Like
  Bayesian Networks
  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 semantic meaning do
not need to have an explanation in terms of cause and effect.
