Dynamic Sampling Procedure for Decomposable Random Networks
Document Type
Conference Proceeding
Publication Date
12-2019
Abstract
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.
Recommended Citation
Li, Haidong; Peng, Yijie; Xu, Xiaoyun; Chen, Chun-Hung; and Heidergott, Bernd F., (2019). Dynamic Sampling Procedure for Decomposable Random Networks. Archīum.ATENEO.
https://archium.ateneo.edu/gsb-pubs/65