Efficient Algorithm for Computing the Ergodic Projector of Markov Multi-chains
| Autor: | Bernd Heidergott, Joost Berkhout |
|---|---|
| Přispěvatelé: | Econometrics and Operations Research, Amsterdam Business Research Institute |
| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
Mathematical optimization
Markov kernel Computer science Markov process Markov Multi-Chains Markov model Continuous-time Markov chain symbols.namesake Series Expansion Approximation Markov renewal process Markov algorithm Ergodic theory SDG 14 - Life Below Water Hidden Markov model General Environmental Science Markov chain mixing time Markov chain Variable-order Markov model Maximum-entropy Markov model Google PageRank Uniformization (probability theory) Social Networks Balance equation symbols General Earth and Planetary Sciences Large-Scale Networks Examples of Markov chains Markov property Markov decision process Forward algorithm Algorithm |
| Zdroj: | Berkhout, J & Heidergott, B F 2015, ' Efficient Algorithm for Computing the Ergodic Projector of Markov Multi-chains ', Procedia Computer Science, vol. 51, pp. 1818-1827 . https://doi.org/10.1016/j.procs.2015.05.403 ICCS Procedia Computer Science, 51, 1818-1827. Elsevier BV |
| ISSN: | 1877-0509 |
| DOI: | 10.1016/j.procs.2015.05.403 |
| Popis: | This paper extends the Markov uni-chain series expansion theory to Markov multi-chains, i.e., to Markov chains having multiple ergodic classes and possible transient states. The introduced series expansion approximation (SEA) provides a controllable approximation for Markov multi-chain ergodic projectors which may be a useful tool in large-scale network analysis. As we will illustrate by means of numerical examples, the new algorithm is faster than the power algorithm for large networks. |
| Databáze: | OpenAIRE |
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