Approximations of non-homogeneous Markov chains on abstract states spaces

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Approximations of nonhomogeneous discrete Markov chains (NDMC) play an essential role in both probability and statistics. In all these settings, it is crucial to consider random variables in appropriate spaces. Therefore, the abstract considerations of such spaces lead to investigating the approximations in ordered Banach space scheme. In this paper, we consider two topologies on the set of NDMC of abstract state spaces. We establish that the set of all uniformly P-ergodic NDMC is norm residual in NDMC. The set of point-wise weak P-ergodic NDMC is also considered and such sets are shown to be a Gδ-subset (in strong topology) of NDMC. We point out that all the deduced results are new in the classical and non-commutative probabilities, respectively, since in most of earlier results the limiting projection is taken as a rank one projection. Indeed, the obtained results give new insight into data-analysis and statistics.

Original languageEnglish
Article number2150002
JournalBulletin of Mathematical Sciences
Volume11
Issue number3
DOIs
Publication statusPublished - Dec 1 2021

Keywords

  • Uniform P -ergodic
  • approximation
  • dense
  • non-homogeneous discrete Markov chain
  • weakly P -ergodic

ASJC Scopus subject areas

  • General Mathematics

Fingerprint

Dive into the research topics of 'Approximations of non-homogeneous Markov chains on abstract states spaces'. Together they form a unique fingerprint.

Cite this