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

Kinetic Ising models are powerful tools for studying the non-equilibriumdynamics of complex systems. As their behavior is not tractable for largenetworks, many mean-field methods have been proposed for their analysis, eachbased on unique assumptions about the system's temporal evolution. Thisdisparity of approaches makes it challenging to systematically advancemean-field methods beyond previous contributions. Here, we propose a unifyingframework for mean-field theories of asymmetric kinetic Ising systems from aninformation geometry perspective. The framework is built on Plefka expansionsof a system around a simplified model obtained by an orthogonal projection to asub-manifold of tractable probability distributions. This view not only unifiesprevious methods but also allows us to develop novel methods that, in contrastwith traditional approaches, preserve the system's correlations. We show thatthese new methods can outperform previous ones in predicting and assessingnetwork properties near maximally fluctuating regimes.

A unifying framework for mean-field theories of asymmetric kinetic Ising systems