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 consider state-aggregation schemes for Markov chains from aninformation-theoretic perspective. Specifically, we consider aggregating thestates of a Markov chain such that the mutual information of the aggregatedstates separated by T time steps is maximized. We show that for T = 1 thisapproach recovers the maximum-likelihood estimator of the degree-correctedstochastic block model as a particular case, thereby enabling us to explaincertain features of the likelihood landscape of this popular generative networkmodel from a dynamical lens. We further highlight how we can uncover coherent,long-range dynamical modules for which considering a time-scale T >> 1 isessential, using synthetic flows and real-world ocean currents, where we areable to recover the fundamental features of the surface currents of the oceans.

State aggregations in Markov chains and block models of networks