On the Effects of Using the Grassmann–Taksar–Heyman Method in Iterative Aggregation–Disaggregation

DOI10.1137/0917021zbMath0840.60062OpenAlexW2048743192MaRDI QIDQ4870655

Tuǧrul Dayar, William J. Stewart

Publication date: 27 March 1996

Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1137/0917021

zbMATH Keywords

Markov chainsGaussian eliminationdecomposabilitystationary probabilityaggregation-disaggregationsparsity schemes

Mathematics Subject Classification ID

Markov chains (discrete-time Markov processes on discrete state spaces) (60J10) Iterative numerical methods for linear systems (65F10) Continuous-time Markov processes on discrete state spaces (60J27) Direct numerical methods for linear systems and matrix inversion (65F05) Software, source code, etc. for problems pertaining to probability theory (60-04)

Related Items (7)

A direct projection method for Markov chains ⋮ Physiology and pathology of iterative aggregation–disaggregation methods ⋮ Componentwise bounds for nearly completely decomposable Markov chains using stochastic comparison and reordering ⋮ Lumpable continuous-time stochastic automata networks. ⋮ Infinite level-dependent QBD processes and matrix-analytic solutions for stochastic chemical kinetics ⋮ Parallel computing for Markov chains with islands and ports ⋮ Convergence theory of some classes of iterative aggregation/disaggregation methods for computing stationary probability vectors of stochastic matrices

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