Information flow in bipartite Markov systems is split into a housekeeping part that maintains correlations through cyclic modes and an excess part that changes mutual information through conservative forces.
We consider a solution to the master equation and the correspond- ing marginal distribution:{p(t)} t∈[0,τ] and{p α(t)}[t∈[0,τ] = {Παp(t)}t∈[0,τ] forα∈ {X, Y}
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Geometric decomposition of information flow: New insights into information thermodynamics
Information flow in bipartite Markov systems is split into a housekeeping part that maintains correlations through cyclic modes and an excess part that changes mutual information through conservative forces.