A particle performs a continuous-time nearest neighbour random walk on a regular triangular lattice inside an angle $\pi / 3$, starting from the corner. See the diagram below. The jump rates are $1 / 3$ from the corner and $1 / 6$ in each of the six directions if the particle is inside the angle. However, if the particle is on the edge of the angle, the rate is $1 / 3$ along the edge away from the corner and $1 / 6$ to each of three other neighbouring sites in the angle. See the diagram below, where a typical trajectory is also shown.

The particle position at time $t \geqslant 0$ is determined by its vertical level $V_{t}$ and its horizontal position $G_{t}$. For $k \geqslant 0$, if $V_{t}=k$ then $G_{t}=0, \ldots, k$. Here $1, \ldots, k-1$ are positions inside, and 0 and $k$ positions on the edge of the angle, at vertical level $k$.

Let $J_{1}^{V}, J_{2}^{V}, \ldots$ be the times of subsequent jumps of process $\left(V_{t}\right)$ and consider the embedded discrete-time Markov chains

$$Y_{n}^{\text {in }}=\left(\widehat{G}_{n}^{\text {in }}, \widehat{V}_{n}\right) \text { and } Y_{n}^{\text {out }}=\left(\widehat{G}_{n}^{\text {out }}, \widehat{V}_{n}\right)$$

where $\widehat{V}_{n}$ is the vertical level immediately after time $J_{n}^{V}, \widehat{G}_{n}^{\text {in }}$ is the horizontal position immediately after time $J_{n}^{V}$, and $\widehat{G}_{n}^{\text {out }}$ is the horizontal position immediately before time $J_{n+1}^{V}$. (a) Assume that $\left(\widehat{V}_{n}\right)$ is a Markov chain with transition probabilities

$$\mathbb{P}\left(\widehat{V}_{n}=k+1 \mid \widehat{V}_{n-1}=k\right)=\frac{k+2}{2(k+1)}, \mathbb{P}\left(\widehat{V}_{n}=k-1 \mid \widehat{V}_{n-1}=k\right)=\frac{k}{2(k+1)},$$

and that $\left(V_{t}\right)$ is a continuous-time Markov chain with rates

$$q_{k k-1}=\frac{k}{3(k+1)}, \quad q_{k k}=-\frac{2}{3}, \quad q_{k k+1}=\frac{k+2}{3(k+1)}$$

[You will be asked to justify these assumptions in part (b) of the question.] Determine whether the chains $\left(\widehat{V}_{n}\right)$ and $\left(V_{t}\right)$ are transient, positive recurrent or null recurrent.

(b) Now assume that, conditional on $\widehat{V}_{n}=k$ and previously passed vertical levels, the horizontal positions $\widehat{G}_{n}^{\text {in }}$ and $\widehat{G}_{n}^{\text {out }}$ are uniformly distributed on $\{0, \ldots, k\}$. In other words, for all attainable values $k, k_{n-1}, \ldots, k_{1}$ and for all $i=0, \ldots, k$,

$$\begin{aligned} &\mathbb{P}\left(\widehat{G}_{n}^{\text {in }}=i \mid \widehat{V}_{n}=k, \widehat{V}_{n-1}=k_{n-1}, \ldots, \widehat{V}_{1}=k_{1}, \widehat{V}_{0}=0\right) \\ &\quad=\mathbb{P}\left(\widehat{G}_{n}^{\text {out }}=i \mid \widehat{V}_{n}=k, \widehat{V}_{n-1}=k_{n-1}, \ldots, \widehat{V}_{1}=k_{1}, \widehat{V}_{0}=0\right)=\frac{1}{k+1} \end{aligned}$$

Deduce that $\left(\widehat{V}_{n}\right)$ and $\left(V_{t}\right)$ are indeed Markov chains with transition probabilities and rates as in (a).

(c) Finally, prove property $(*)$.