Table of Contents
Entropy, Volume 19, Issue 12 (December 2017)
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Cover Story (view full-size image) The combined application of the Bayesian Model Selection and Maximum Entropy sharply differentiates [...] Read more. The combined application of the Bayesian Model Selection and Maximum Entropy sharply differentiates between irrelevant and relevant observables and identifies models that subsume falsifiable predictions on how the former “depend” on the latter. This makes it possible to deduce, in an unbiased manner, what type of interactions (e.g., pairwise) better describe the statistical dependencies between variables in a dataset. For example, pairwise (red) and four body interactions (green) turn out to be relevant to describe the voting behavior of the US Supreme Court. View Paper here.