SALZA: Soft algorithmic complexity estimates for clustering and causality inference

Marion Revolle; Cayre François; Nicolas Le Bihan

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arXiv · 1607.05144

SALZA: Soft algorithmic complexity estimates for clustering and causality inference

Marion Revolle·Cayre François·Nicolas Le Bihan

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Abstract

A complete set of practical estimators for the conditional, simple and joint algorihmic complexities is presented, from which a semi-metric is derived. Also, new directed information estimators are proposed that are applied to causality inference on Directed Acyclic Graphs. The performances of these estimators are investigated and shown to compare well with respect to the state-of-the-art Normalized Compression Distance (NCD).

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Marion Revolle, Cayre François, Nicolas Le Bihan. 2016-07-18. SALZA: Soft algorithmic complexity estimates for clustering and causality inference. https://arxiv.org/abs/1607.05144

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