SALZA: Soft algorithmic complexity estimates for clustering and causality inference
Information Theory
2016-07-19 v1 math.IT
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).
Cite
@article{arxiv.1607.05144,
title = {SALZA: Soft algorithmic complexity estimates for clustering and causality inference},
author = {Marion Revolle and Cayre François and Nicolas Le Bihan},
journal= {arXiv preprint arXiv:1607.05144},
year = {2016}
}