I would like to calculate the mixing time of a continuous time starting from the **rate matrix** and **not** necessarily assuming that the time in between jumps have rate 1 - all I have is the (finite dimensional) transition rate matrix $Q$ for this system. I am fine with other basic assumptions about the chain (e.g., irreducible). The only references I can find (e.g., here) do not seem to cover this case. Is there a theorem or transformation that covers this case?

**UPDATE**
It seems that by uniformization, I can turn my continuous time Markov chain with different jump rates $\gamma_i$ for each element in my state space $\Omega$ (defined by the generator matrix $Q$) into a continuous time Markov chain with transition matrix $P$ (which is now not a generator matrix) and jump rate $\gamma \geq \sup_i | \gamma_i |$. So, my question above is really:

**How can the methods for finding mixing times with transition times distributed as an exponential random variable of mean 1 be generalized to the case of where the transition time are distributed according to an exponential random variable with arbitrary (positive, real valued) mean $\gamma$**?

Really, I think this just means transforming a CTMC with rate $\gamma$ into a CTMC with rate 1 that has the same dynamics, but I don't really know how to do this.

**UPDATE**
I figured it out: uniformization let's you choose any value for the rate, larger than the slowest rate, which works out in my case, since my rates are all less than 1.