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Consider a Markov process on a finite state space $S$, whose dynamic is determined by a certain infinitesimal generator $Q$ (that is a matrix in this case) and an initial distribution $m$.

1) Is there anything general that can be said on the spectrum of the matrix $-Q$?

2) Is there any bounds on the eigenvalues of $-Q$? And on their ratios?

I am interested in linear algebra results as well as more probabilistic ones, maybe using information about the structure of the state space or on the process itself. If can be of any help, for the problem I am addressing, I usually have a quite big state space and the Q-matrix is quite sparse, but unfortunately without any symmetry or nice structure.

For discrete-time Markov chains there is an huge literature linking properties of the spectrum of the transition matrix $P$ to various mixing times, geometric properties of the state space, etc. Of course one can move from the continuous setting to the discrete one via uniformization and thus "translate" most of these results, but I was wondering the following

3) Is there any bounds on the eigenvalues developed specifically for the continuous time setting? And generally speaking, does anyone know any references for such continuous-time theory?

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    For each matrix intensity $Q$ there is a corresponding stochastic matrix $P$ of the underlying discrete-time Markov Chain. All the infinite-time horizon properties of the continuous time Markov Chain can be verified on the underlying discrete-time one since intensity does not matter there, only the structure. I'll try to found the formula how to obtain $P$ from $Q$ so you can study $Q$ by studying classical results for $P$2012-02-08

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