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Analysis of non-reversible Markov chains via similarity orbits

Part of: Markov processes Ergodic theory

Published online by Cambridge University Press:  18 February 2020

Michael C. H. Choi  and
Pierre Patie
Michael C. H. Choi*
Affiliation:
Institute for Data and Decision Analytics, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, PR China and Shenzhen Institute of Artificial Intelligence and Robotics for Society
Pierre Patie
Affiliation:
School of Operations Research and Information Engineering, Cornell University, Ithaca, NY 14853, USA and Laboratoire de Mathématiques et leurs Applications, UMR CNRS 5142, Université de Pau et des Pays de l’Adour, Pau, France
*
*Corresponding author. Email: [email protected]
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Abstract

In this paper we develop an in-depth analysis of non-reversible Markov chains on denumerable state space from a similarity orbit perspective. In particular, we study the class of Markov chains whose transition kernel is in the similarity orbit of a normal transition kernel, such as that of birth–death chains or reversible Markov chains. We start by identifying a set of sufficient conditions for a Markov chain to belong to the similarity orbit of a birth–death chain. As by-products, we obtain a spectral representation in terms of non-self-adjoint resolutions of identity in the sense of Dunford [21] and offer a detailed analysis on the convergence rate, separation cutoff and L2-cutoff of this class of non-reversible Markov chains. We also look into the problem of estimating the integral functionals from discrete observations for this class. In the last part of this paper we investigate a particular similarity orbit of reversible Markov kernels, which we call the pure birth orbit, and analyse various possibly non-reversible variants of classical birth–death processes in this orbit.

MSC classification

Primary: 60J05: Discrete-time Markov processes on general state spaces 60J10: Markov chains (discrete-time Markov processes on discrete state spaces) 60J27: Continuous-time Markov processes on discrete state spaces
Secondary: 60J20: Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) 37A25: Ergodicity, mixing, rates of mixing 37A30: Ergodic theorems, spectral theory, Markov operators
Type
Paper
Information
Combinatorics, Probability and Computing , Volume 29 , Issue 4 , July 2020 , pp. 508 - 536
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Copyright
© Cambridge University Press 2020

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