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For each adjacent pair, the detailed balance equations follows directly:For nonadjacent distinct states both fluxes vanish, and diagonal fluxes agree trivially. Since ${\sum }_n{\pi }_n=e^{-\lambda }{\sum }_n{\lambda }^n/n!=1$, this is a genuine stationary distribution, rather than merely an invariant measure of infinite mass.

Every upward and downward transition probability between neighbouring states is positive for $\lambda >0$. Any two states can therefore be joined by a finite path of positive probability: the Markov chain is irreducible. Moreover,State zero has period one; Markov chain irreducibility makes every state aperiodic. Finally, the standard countable-state recurrence theorem states that an irreducible Markov chain admitting a stationary distribution of total mass one is positive recurrent, with that stationary distribution unique. The countable-state Markov chain convergence theorem then states that Markov chain irreducibility, positive recurrence and aperiodicity imply $P^k(i,j)\to {\pi }_j$ for all states $i,j$. The hypotheses just verified hold on the infinite state space ${\mathbb{N}}_0$, not merely on a numerical truncation.^[ref]

Consequently, the unique stationary distribution and the limiting distribution are both $\mathrm{Poisson}(\lambda )$, from every initial state. The countable-state Markov chain ergodic theorem also gives $N^{-1}{\sum }_{k=1}^{N}f(X_k)\to {\sum }_n{\pi }_{n}f(n)$ almost surely whenever ${\sum }_n{\pi }_n|f(n)|<\infty$.^[ref]

The restriction $Q_C$ is a finite Markov chain that is an irreducible Markov chain. It is aperiodic, because its upper states have self-transition probability $a/(a+b)>0$, and the period is constant on a communicating class. We now find its stationary distribution.

Every column of $Q_C$ sums to one. Here is the full incoming-mass check, with each total measured in units of $1/(a+b)$. For $j\in L$, the incoming contributions are $a$ from $j+b\in I$ and $b$ from the self-transition. For $j\in U$, they are $b$ from $j-a\in I$ and $a$ from the self-transition.

For $j\in I$, the upward predecessor $j-a$ lies in $I$ exactly when $j\ge -c+a$, contributing $b$ units. When $j\le -c+a-1$, that predecessor is replaced by resets from all $b$ states of $L$, again contributing $b$ units. Independently, the downward predecessor $j+b$ contributes $a$ units when $j\le c-b$; when $j\ge c-b+1$, resets from the $a$ states of $U$ contribute those $a$ units instead. Thus every column total is $a+b$ units.

Therefore $Q_C$ is a doubly stochastic matrix, and the uniform distribution on $C$ is stationary:The countable-state Markov chain convergence theorem, applied to the finite closed class $C$, gives $Q_C^n(i,j)\to {\pi }_j$ for every $i\in C$. Its hypotheses hold: $C$ is finite and irreducible, hence positive recurrent, and it is aperiodic.

If $i_0>c+a$, the hitting time $H$ of $C$ has $\mathbb{P}(H>n)=(a/(a+b){)}^n$. If $i_0<-c-b$, the corresponding tail is $(b/(a+b){)}^n$. Thus $H<\infty$ almost surely from every exterior initial state. Conditioning on $H$ and $R_H$, use convergence within $C$ after entrance and the bounded convergence theorem; equivalently, first restrict to $H\le K$ and then let $K\to \infty$. This proves, for every $i_0\in \mathbb{Z}$,The limiting distribution is independent of $i_0$.

Let $d=\gcd (a,b)>1$ and put $a^{\prime }=a/d$, $b^{\prime }=b/d$. Then $a^{\prime },b^{\prime }$ are coprime integers, and $p=b^{\prime }/(a^{\prime }+b^{\prime })$. The six requested revisions are as follows.

(b)(i): communicating classes. Every increment is divisible by $d$, so the residue class modulo $d$ is preserved. Conversely, if $j-i$ is divisible by $d$, Bézout's identity applied to $a^{\prime },b^{\prime }$ gives a path just as in (b)(i). Thus the original Markov chain is reducible on $\mathbb{Z}$, with exactly the $d$ closed communicating classesThe specified walk starting at zero stays in $d\mathbb{Z}$.

(b)(ii): period of a state in a Markov chain. The return equation $au=bv$ reduces to $a^{\prime }u=b^{\prime }v$. Hence $u=b^{\prime }k$, $v=a^{\prime }k$. For every state, the period of a state in a Markov chain is thereforeThis is still at least two, so no state is aperiodic.

(b)(iii): stationary distribution. The conclusion is unchanged: there is no stationary distribution on $\mathbb{Z}$. The maximum-mass proof in (b)(iii) applies verbatim, without using coprimality. Equivalently, each closed communicating class, after translating and dividing its state labels by $d$, is the same centered random walk with step sizes $a^{\prime },-b^{\prime }$ and has no stationary distribution.

(b)(iv): occupation of zero. On $d\mathbb{Z}$, the rescaled walk $S_n/d$ has exactly the transition probabilities of the coprime walk with parameters $a^{\prime },b^{\prime }$. The return-probability argument in (b)(iv) therefore makes this class null recurrent. The renewal process proof of the occupation limit applies unchanged:Starting in another residue class would make every visit to zero impossible, also giving occupation proportion zero.

(c)(i): modified communicating classes. These are unchanged: $C=\{-c-b,\dots ,c+a\}$ is one finite closed communicating class, and all other states are transient singleton classes. The original proof used consecutive reset destinations and never used coprimality. Resetting to the $b$ consecutive integers $c-b+1,\dots ,c$ connects different residue classes, so it would be incorrect to split $C$ according to residue classes modulo $d$.

(c)(ii): limiting distribution. The column-sum calculation, the irreducible Markov chain property on a finite class, positive self-transition probabilities, and geometric entrance argument also required no coprimality. Thus for every initial state the same convergence in distribution holds:In particular, the denominator $2c+a+b+1$ is unchanged; it is not divided by $d$.