Markov chain sensitivity measured by mean first passage times

100 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Gene perturbation and intervention in probabilistic Boolean networks (2002, background): “Cho and Meyer (2000) also showed that their bound is tight in the sense that there always exists a perturbation E that attains the bound.”
Logarithmic Online Regret Bounds for Undiscounted Reinforcement Learning (2006, unknown): “Theorem 1 (Cho, Meyer[16]).”
Comparison of perturbation bounds for the stationary distribution of a Markov chain (2001, methodology): “[27] R. Whittaker, Experiments with radisphosphorus tracer in aquarium microcosms, Ecological Monographs, 31 (1961), pp. 157–188.”
From Boolean to probabilistic Boolean networks as models of genetic regulatory networks (2002, background): “Using recent results from perturbation theory of stochastic matrices [97], an explicit bound on the steady-state probabil-”
Deeper Inside PageRank (2004, methodology): “More rigorous measures of eigenvector sensitivity for Markov chains were developed by Meyer and Stewart [91], Meyer and Golub [57], Cho and Meyer [34], and Funderlic and Meyer [54].”