Approximation of Markov Processes by Lower Dimensional Processes via Total Variation Metrics
Artikel i vetenskaplig tidskrift, 2017

The aim of this paper is to approximate a Finite-State Markov (FSM) process by another process defined on a lower dimensional state space, called the approximating process, with respect to a total variation distance fidelity criterion. The approximation problem is formulated as an optimization problem using two different approaches. The first approach is based on approximating the transition probability matrix of the FSM process by a lower-dimensional transition probability matrix, resulting in an approximating process which is a Finite-State Hidden Markov (FSHM) process. The second approach is based on approximating the invariant probability vector of the original FSM process by another invariant probability vector defined on a lower-dimensional state space. Going a step further, a method is proposed based on optimizing a Kullback-Leibler divergence to approximate the FSHM processes by FSM processes. The solutions of these optimization problems are described by optimal partition functions which aggregate the states of the FSM process via a corresponding water-filling solution, resulting in lower-dimensional approximating processes which are FSHM or FSM processes. Throughout the paper, the theoretical results are justified by illustrative examples that demonstrate our proposed methodology.

Markov process


total variation distance



Approximating process

Automation & Control Systems



I. Tzortzis

University of Cyprus

C. D. Charalambous

University of Cyprus

Themistoklis Charalambous

Chalmers, Signaler och system, Kommunikation, Antenner och Optiska Nätverk

C. N. Hadjicostis

University of Cyprus

Mikael Johansson

Kungliga Tekniska Högskolan (KTH)

IEEE Transactions on Automatic Control

0018-9286 (ISSN) 1558-2523 (eISSN)

Vol. 62 3 1030-1045 7487050



Sannolikhetsteori och statistik

Matematisk analys



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