Spectral Theory of Discrete Processes
Palle E. T. Jorgensen, Myung-Sin Song

TL;DR
This paper develops a spectral analysis framework for transfer operators associated with various stochastic processes, including random walks, wavelet algorithms, and fractal measures, focusing on their harmonic functions and spectral properties.
Contribution
It introduces methods to realize and analyze transfer operators as (possibly unbounded) non-normal operators in suitable Hilbert spaces, applicable to a broad class of stochastic processes.
Findings
Spectral analysis of transfer operators for diverse stochastic processes.
Characterization of processes governed by a single transfer operator.
Insights into spectral properties of non-normal, possibly unbounded operators.
Abstract
We offer a spectral analysis for a class of transfer operators. These transfer operators arise for a wide range of stochastic processes, ranging from random walks on infinite graphs to the processes that govern signals and recursive wavelet algorithms; even spectral theory for fractal measures. In each case, there is an associated class of harmonic functions which we study. And in addition, we study three questions in depth: In specific applications, and for a specific stochastic process, how do we realize the transfer operator as an operator in a suitable Hilbert space? And how to spectral analyze once the right Hilbert space has been selected? Finally we characterize the stochastic processes that are governed by a single transfer operator. In our applications, the particular stochastic process will live on an infinite path-space which is realized in turn on a…
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