Markov dualities via the spectral decompositions of the two Markov generators in their bi-orthogonal basis of right and left eigenvectors
Cecile Monthus

TL;DR
This paper revisits Markov duality through spectral decompositions of generators, revealing new insights into classical dualities and enabling the construction of novel dual processes using eigenvector analysis.
Contribution
It introduces a spectral framework for understanding Markov dualities, connecting eigenvectors to duality functions, and demonstrates how to construct new dual processes from eigenvalues.
Findings
Spectral perspective clarifies classical dualities like time-reversal and Siegmund duality.
Eigenvector analysis links duality functions to right and left eigenvectors of generators.
Constructs new dual processes based on eigenvalues, exemplified with Wright-Fisher and Kingman models.
Abstract
The notion of Markov duality between two Markov processes that can live in two different configurations spaces is revisited via the spectral decompositions of the two Markov generators in their bi-orthogonal basis of right and left eigenvectors. In this formulation, the two generators should have the same eigenvalues that may be complex, while the duality function can be considered as a mapping between the right and the left eigenvectors of the two models. We describe how this spectral perspective is useful to better understand two well-known dualities between processes defined in the same configuration space: the Time-Reversal duality corresponds to an exchange between the right and the left eigenvectors that involves the steady state, while in the Siegmund duality, the left eigenvectors correspond to integrals of the dual right…
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