# Markov chains. If $X_0=0$ then the probability that $X_n\ge1$ for all $n\ge 1$ is $\frac{6}{\pi^2}$

Let $$(X_n)_{n\ge0}$$ be a markov chain on $$\{0,1,...\}$$ with transition probabilities given by

$$p_{01}=1,p_{i,i+1}+p_{i,i-1}=1, p_{i,i+1}=\Big(\frac{i+1}{i}\Big)^2p_{i,i-1}, i\ge1$$

I need to show that if $$X_0=0$$ then the probability that $$X_n\ge1$$ for all $$n\ge 1$$ is $$\frac{6}{\pi^2}$$

I'm looking for tips on how to solve this task.

Here is a rather long answer but it has the merit of being quite general I think.

Minimality of the hitting probability

In the general case of a Markov chain with state space $$\mathbb{N}$$ and transitions $$(p_{i,j})_{(i,j)\in\mathbb{N}^2}$$, consider $$\mathcal{P} \subset \mathbb{N}$$ a subset of states and for all $$i \in\mathbb{N}$$, $$(X_n(i))$$ a Markov chain starting from $$i$$ and the hitting probability $$h_{\mathcal{P}}(i) = \mathbb{P}\big(\inf\{n\ge 0, X_n(i) \in \mathcal{P}\} < +\infty\big)$$. Now we have that $$h_{\mathcal{P}} \mbox{ is the minimal non negative solution to }\quad h(i)=\left\{\begin{array}{ll} 1 \mbox{ if } i \in \mathcal{P}\\ \sum \limits_{j \in \mathbb{N}} p_{i,j}h(j) \mbox{ otherwise.}\end{array}\right.$$

Indeed, $$h_{\mathcal{P}}$$ is a solution. Conversely, if $$h$$ is a solution, for $$i \in \mathbb{N} \backslash \mathcal{P}$$, $$h(i) = \sum \limits_{j \in \mathcal{P}} p_{i,j} + \sum \limits_{j \in \mathbb{N}\backslash \mathcal{P}} p_{i,j}h(j)$$. Plugging it back, we also get $$h(i) = \sum \limits_{j \in \mathcal{P}} p_{i,j} + \sum \limits_{j \in \mathbb{N}\backslash\mathcal{P}}p_{i,j}\sum \limits_{k \in \mathcal{P}}p_{j,k} + \sum \limits_{j \in \mathbb{N}\backslash\mathcal{P}}p_{i,j} \sum \limits_{k \in \mathbb{N}\backslash \mathcal{P}} p_{j,k}h(k)$$. Repeat several times and note that the last term is non negative: you get $$h(i) \ge \mathbb{P}(X_1 \in \mathcal{P})+\mathbb{P}(X_1 \notin \mathcal{P},X_2\in\mathcal{P})+\cdot\cdot\cdot+\mathbb{P}(X_1\notin\mathcal{P},...,X_{n-1}\notin\mathcal{P},X_n\in\mathcal{P}).$$ Taking the limit, $$h(i) \ge h_{\mathcal{P}}(i)$$ using the definition of $$h_{\mathcal{P}}$$.



I will change a bit your problem. We are going to see $$0$$ as a well, and the random walk starts from $$1$$. So $$X_0=1$$ and $$p_{0,0} = 1$$. Nothing else is changed.
In your problem the hitting states are just $$\mathcal{P} = \{0\}$$, the transition are given as you stated. Let us define and compute the sequence $$u_n = h_{\mathcal{P}}(n)$$, the probability of hitting $$0$$ starting from $$n$$. We have $$u_0 = 1$$ (if you start in the well $$0$$, you hit it) and for all $$n \ge 1$$, the law of total probability yields $$u_n = p_{n,n-1}u_{n-1}+p_{n,n+1}u_{n+1} = \frac{n^2}{n^2+(n+1)^2}u_{n-1}+\frac{(n+1)^2}{n^2+(n+1)^2}u_{n+1}$$. Regrouping gives you $$(n+1)^2 (u_{n+1}-u_n) = n^2 (u_n-u_{n-1})$$ so $$u_{n+1}-u_n = \Big(\prod \limits_{k=1}^n \frac{k^2}{(k+1)^2}\Big)(u_1-u_0)=\frac{1}{(n+1)^2}(u_1-1)$$.
Summing, we get $$u_n-u_0 = \sum \limits_{k=0}^{n-1} \frac{1}{(k+1)^2}(u_1-1)$$ i.e. $$u_n = 1 - (1-u_1) \sum \limits_{k=1}^n \frac{1}{k^2}.$$
Now use the first section: among all possible choices of $$u_1$$, it has to be the one that makes $$(u_n)$$ minimal and non negative. Remind that $$\sum \limits_{k=1}^n \frac{1}{k^2} \underset{n \to \infty}{\longrightarrow} \frac{\pi^2}{6}$$. If we had $$u_1 < 1-\frac{6}{\pi^2}$$, $$(u_n)$$ would not be non-negative, and with $$u_1 > 1-\frac{6}{\pi^2}$$ it would not be the minimal solution (because a $$u_1'$$ between $$1-\frac{6}{\pi^2}$$ and $$u_1$$ would give a smaller solution).
That gives you $$u_1 = 1-\frac{6}{\pi^2}$$. The probability of going back to zero starting from $$1$$ is $$1-\frac{6}{\pi^2}$$. Additionnally, we found that the probabilty of going back to zero starting from $$n$$ is $$1 - \frac{6}{\pi^2} \sum \limits_{k=1}^n \frac{1}{k^2}$$.