Name
Abstract | ||
|---|---|---|
Motivated by the computational difficulty of analyzing very large Markov chains, we define a notion of clusters in (not necessarily reversible) Markov chains, and explore the possibility of analyzing a cluster “in vitro,” without regard to the remainder of the chain. We estimate the stationary probabilities of the states in the cluster using only transition information for these states, and bound the error of the estimate in terms of parameters measuring the quality of the cluster. Finally, we relate our results to searching in a hyperlinked environment, and provide supporting experimental results. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11682462_9 | LATIN |
Keywords | Field | DocType |
stationary probability,transition information,hyperlinked environment,markov chain,large markov chain,computational difficulty | Cluster (physics),Markov chain mixing time,Computer science,Markov chain,Remainder,Algorithm,Balance equation,Stationary distribution,Stationary state,Examples of Markov chains | Conference |
Volume | ISSN | ISBN |
3887 | 0302-9743 | 3-540-32755-X |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nir Ailon | 1 | 1114 | 70.74 |
| Steve Chien | 2 | 323 | 19.12 |
| Cynthia Dwork | 3 | 9137 | 821.87 |