A Generalized Framework For Kullback-Leibler Markov Aggregation

Rana Ali Amjad, Clemens Blöchl, Bernhard Geiger*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


We propose an information-theoretic Markov aggregation framework that is motivated by two objectives: 1) The Markov chain observed through the aggregation mapping should be Markov. 2) The aggregated chain should retain the temporal dependence structure of the original chain. We analyze our parameterized cost function and show that it contains previous cost functions as special cases, which we critically assess. Our simple optimization heuristic for deterministic aggregations characterizes the optimization landscape for different parameter values.
Original languageEnglish
Article number8861026
Pages (from-to)3068-3075
Number of pages8
JournalIEEE Transactions on Automatic Control
Issue number7
Publication statusPublished - Jul 2020


  • Bisimulation
  • information theory
  • lumpability
  • Markov chain
  • model reduction
  • predictability

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Science Applications


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