Integrated information and variational inference provide influential mathematical frameworks in neuroscience. Yet, the understanding of the connection between the two is limited. Here, we study a minimal model to show how variational inference displays large integrated information for highly correlated target distributions, in contrast with alternative inference approaches like maximum likelihood estimation.

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