{"paper":{"title":"Point Divergence Gain and Multidimensional Data Sequences Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.data-an","authors_text":"Dalibor \\v{S}tys, Jan Korbel, Petr Mach\\'a\\v{c}ek, Renata Rycht\\'arikov\\'a","submitted_at":"2017-12-30T19:31:59Z","abstract_excerpt":"We introduce novel information-entropic variables -- a Point Divergence Gain (${\\Omega}^{(l \\rightarrow m)}_\\alpha$), a Point Divergence Gain Entropy ($I_\\alpha$), and a Point Divergence Gain Entropy Density ($P_\\alpha$) -- which are derived from the R\\'{e}nyi entropy and describe spatio-temporal changes between two consecutive discrete multidimensional distributions. The behavior of ${\\Omega}^{(l \\rightarrow m)}_\\alpha$ is simulated for typical distributions and, together with $I_\\alpha$ and $P_\\alpha$, applied in analysis and characterization of series of multidimensional datasets of compute"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.00183","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}