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The measure total correlation is defined making use of Shannon's entropy: $$ TC(X_1,\dots,X_m) = \sum_{i} H(X_i) - H(X_1,\dots,X_m) $$ This comes also with different names: e.g. multi information, or redundancy.

Is there a paper that discusses the generalization of total correlation using the Renyi entropy? Where the Renyi entropy is defined as: $$ H_{\alpha}(X) = \frac{1}{1-\alpha} log{\sum_x p(x)^{\alpha}} $$ So far I found that the generalization is possible: Generalized Redundancies for Time Series Analysis (1995). However, no deep insights are discussed in this paper.

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