An overview of statistical and information-theoretic aspects of hidden Markov processes
(HMPs) is presented. An HMP is a discrete-time finite-state homogeneous Markov chain …

Emergence and causality are two fundamental concepts for understanding complex
systems. They are interconnected. On one hand, emergence refers to the phenomenon …

This paper investigates robust H 2 and H∞ step tracking control methods for networked
control systems subject to random time delays modeled by Markov chains. To make full use …

This paper is concerned with model reduction for complex Markov chain models. The
Kullback-Leibler divergence rate is employed as a metric to measure the difference between …

While the estimation of the parameters of a hidden Markov model has been studied
extensively, the consistent estimation of the number of hidden states is still an unsolved …

The problem of reducing a hidden Markov model (HMM) to one of smaller dimension that
exactly reproduces the same marginals is tackled by using a system-theoretic approach …

One of the challenges in the use of Markov model in reliability analysis is the exponential
growth of number of states and transition rates when the system gets complex. In reliability …

In this paper, we address whether a (probabilistic) function of a finite homogeneous Markov
chain still enjoys a Markov-type property. We propose a complete answer to this question …

In this paper, we present a method for reducing a regular, discrete-time Markov chain
(DTMC) to another DTMC with a given, typically much smaller number of states. The cost of …

Dynamic models of nanometer‐scale phenomena often require an explicit consideration of
interactions among a large number of atoms or molecules. The corresponding mathematical …