 Solution

Let $P$ be the Markov kernel and $\mu$ its invariant probability measure. Using the conventiongeometric ergodicity means that there are $r\in (0,1)$ and a finite measurable state-dependent prefactor $M(x)$ such thatHere total variation distance is used, and the rate $r$ does not depend on $x$. The prefactor need not be uniformly bounded; that stronger property is uniform geometric ergodicity. For a deterministic starting state, this estimate must hold at that state. If the definition is made only $\mu$-almost everywhere, the subsequent assertions concern those starting states where the prefactor is finite. The reversible Markov chain assumption means $\mu (dx)P(x,dy)=\mu (dy)P(y,dx)$.