Dynamic Sampling Procedure for Decomposable Random Networks
This research studies the problem of node ranking in a random network. Specifically, we consider a Markov chain with several ergodic classes and unknown transition probabilities which can be estimated by sampling. The objective is to select all of the best nodes in each ergodic class. A sampling pro...
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| Main Authors: | , , , , |
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| 格式: | text |
| 出版: |
Archīum Ateneo
2019
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| 主題: | |
| 在線閱讀: | https://archium.ateneo.edu/gsb-pubs/65 https://ieeexplore.ieee.org/abstract/document/9004795 |
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| 機構: | Ateneo De Manila University |
| 總結: | This research studies the problem of node ranking in a random network. Specifically, we consider a Markov chain with several ergodic classes and unknown transition probabilities which can be estimated by sampling. The objective is to select all of the best nodes in each ergodic class. A sampling procedure is proposed to decompose the Markov chain and maximize a weighted probability of correct selection of the best nodes in each ergodic class. Numerical results demonstrate the efficiency of the proposed sampling procedure. |
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