English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

We describe a class of deterministic weakly chaotic dynamical systems with infinite memory. These "herding systems" combine learning and inference into one algorithm, where moments or data-items are converted directly into an arbitrarily long sequence of pseudo-samples. This sequence has infinite range correlations and as such is highly structured. We show that its information content, as measured by sub-extensive entropy, can grow as fast as K log T, which is faster than the usual ½K log T for exchangeable sequences generated by random posterior sampling from a Bayesian model. In one dimension we prove that herding sequences are equivalent to Sturmian sequences which have complexity exactly log(T + 1). More generally, we advocate the application of the rich theoretical framework around nonlinear dynamical systems, chaos theory and fractal geometry to statistical learning.

Statistical inference using weak chaos and infinite memory