For an irreducible stochastic matrix T, the Kemeny constant K(T) measures the expected time to mixing of the Markov chain corresponding to T. Given a strongly connected directed graph D, we consider the set ΣD of stochastic matrices whose directed graph is subordinate to D, and compute the minimum value of K, taken over the set ΣD. The matrices attaining that minimum are also characterised, thus yielding a description of the transition matrices in ΣD that minimise the expected time to mixing. We prove that K(T) is bounded from above as T ranges over the irreducible members of ΣD if and only if D is an intercyclic directed graph, and in the case that D is intercyclic, we find the maximum value of K on the set ΣD. Throughout, our re...
Author names in alphabetical order. Submitted to SIAM Review, problems and techniques section. We co...
Given an irreducible discrete time Markov chain on a finite state space, we consider the largest exp...
AbstractLet T∈Rn×n be an irreducible stochastic matrix with stationary distribution vector π. Set A=...
AbstractFor an irreducible stochastic matrix T, the Kemeny constant K(T) measures the expected time ...
For an irreducible stochastic matrix T, the Kemeny constant K(T) measures the expected time to mixi...
A quantity known as the Kemeny constant, which is used to measure the expected number of links that...
Suppose that T is an n×n stochastic matrix, and denote its directed graph by D(T). The function τ(T)...
In a finite m-state irreducible Markov chain with stationary probabilities {πi} and mean first passa...
Given an irreducible stochastic matrix M, Kemeny’s constant K(M) measures the expected time for the ...
AbstractFor an irreducible stochastic matrix T, we consider a certain condition number c(T), which m...
A quantity known as the Kemeny constant, which is used to measure the expected number of links that ...
Consider a finite state irreducible Markov process with transition graph G and invariant probability...
We consider the problem of assigning transition probabilities to the edges of a path in such a way t...
We consider irreducible Markov chains on a finite state space. We show that the mixing time of any s...
AbstractMixing time quantifies the convergence speed of a Markov chain to the stationary distributio...
Author names in alphabetical order. Submitted to SIAM Review, problems and techniques section. We co...
Given an irreducible discrete time Markov chain on a finite state space, we consider the largest exp...
AbstractLet T∈Rn×n be an irreducible stochastic matrix with stationary distribution vector π. Set A=...
AbstractFor an irreducible stochastic matrix T, the Kemeny constant K(T) measures the expected time ...
For an irreducible stochastic matrix T, the Kemeny constant K(T) measures the expected time to mixi...
A quantity known as the Kemeny constant, which is used to measure the expected number of links that...
Suppose that T is an n×n stochastic matrix, and denote its directed graph by D(T). The function τ(T)...
In a finite m-state irreducible Markov chain with stationary probabilities {πi} and mean first passa...
Given an irreducible stochastic matrix M, Kemeny’s constant K(M) measures the expected time for the ...
AbstractFor an irreducible stochastic matrix T, we consider a certain condition number c(T), which m...
A quantity known as the Kemeny constant, which is used to measure the expected number of links that ...
Consider a finite state irreducible Markov process with transition graph G and invariant probability...
We consider the problem of assigning transition probabilities to the edges of a path in such a way t...
We consider irreducible Markov chains on a finite state space. We show that the mixing time of any s...
AbstractMixing time quantifies the convergence speed of a Markov chain to the stationary distributio...
Author names in alphabetical order. Submitted to SIAM Review, problems and techniques section. We co...
Given an irreducible discrete time Markov chain on a finite state space, we consider the largest exp...
AbstractLet T∈Rn×n be an irreducible stochastic matrix with stationary distribution vector π. Set A=...