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For a continuous-time Markov chain whose jump times tend to infinity, the chain and its jump chain have the same directed paths of positive-probability jumps. They are therefore irreducible simultaneously, and a state is recurrent for one exactly when it is recurrent for the other: the event of returning after departure depends only on the sequence of visited states.

Let $T_0=0$ and let $T_{r+1}$ be the first visit to $i$ strictly after $T_r$, with $T_{r+1}=\infty$ if there is none. Put $q={\mathbb{P}}_i(T_1<\infty )$. The Strong Markov property at each finite first return time gives, by induction,The number of visits, including time zero, is $N={\sum }_{r\ge 0}{1}_{\{T_r<\infty \}}={\sum }_{n\ge 0}{1}_{\{X_n=i\}}$. By Tonelli theorem for nonnegative terms,If $q<1$, this geometric series is $1/(1-q)$, and $1-q$ is the probability of never returning. If $q=1$, every term of the final series is one and both sides are infinite. Thuswith the prescribed infinite-value convention. This covers both transient states and recurrent states.

Let $R$ be the number of returns to $v$ after time $0$. Since $v$ is a recurrent state, the probability of another return after each visit is one. By the stated geometric distribution description, ${\mathbb{P}}_v(R\ge k)=1$ for every positive integer $k$. Thus $R=\infty$ almost surely and ${\mathbb{E}}_{v}R=\infty$.

On the other hand, writing each visit as an indicator random variable and applying the monotone convergence theorem to the partial sums givesThe left side is infinite, proving the recurrence criterion by return probabilities in the required direction:

A continuous-time Markov chain is irreducible if every state can reach every other state with positive probability. A state $i$ is recurrent if, after leaving $i$, the chain returns to $i$ with probability one; an irreducible chain is recurrent if one, and hence every, state is recurrent. A recurrent state is positive recurrent if its expected continuous first return time is finite, and an irreducible chain is positive recurrent when all its states are.

Write $q_i=-g_{ii}$ for the total rate of leaving $i$. The jump chain of a continuous-time Markov chain $X$ is the discrete-time chain $Y=(Y_n)$ recording the states occupied after successive jumps. Its transition probabilities arewhenever $q_i>0$.

The chain $X$ is irreducible if every state can be reached from every other state with positive probability. A state $i$ is recurrent if, after leaving $i$, the process returns to $i$ with probability one; an irreducible chain is recurrent when one, and hence every, state is recurrent.

The continuous-time Markov chain and its jump chain have exactly the same successive states. If $Y$ is transient, it visits each state only finitely often, so $X$ is transient. If $Y$ is recurrent, it visits a fixed state $i$ infinitely often. At those visits, $X$ has independent exponential holding times of rate $q_i<\infty$. Their sum diverges almost surely, so explosion cannot occur before all those returns. Hence $X$ returns to $i$ infinitely often and is recurrent. Thus, for an irreducible chain,

For the simple random walk on the integer line, a return to the origin is possible only at an even time. At time $2n$, exactly $n$ of the increments must be $+1$, so the return probability isThe supplied factorial bounds, equivalently the order estimate in the Stirling formula, giveConsequently ${\sum }_n{p}_{2n}$ diverges. By the recurrence criterion by return probabilities, the origin is a recurrent state; translation invariance then makes the whole walk recurrent.

For three independent walks, the probability that all three are at the origin at time $2n$ isThis P-series is convergent, so the first of the Borel-Cantelli lemmas says that simultaneous returns occur only finitely often with probability one. Therefore the requested probability is

A state is transient when its probability of ever returning is less than one.