  Given a transition matrix $P$ indexed by a finite set $V$ of vertices, the
voter model is a discrete-time Markov chain in $\{0,1\}^V$ where at each
time-step a randomly chosen vertex $x$ imitates the opinion of vertex $y$ with
probability $P(x,y)$. The noisy voter model is a variation of the voter model
in which vertices may change their opinions by the action of an external noise.
The strength of this noise is measured by an extra parameter $p \in [0,1]$. The
noisy voter model is a Markov chain with state space $\{0,1\}^V$, and has a
unique stationary distribution $\Gamma$ when $p > 0$. In this paper, we study
$\Gamma$ when the number of vertices in $V$ tends to infinity. In particular we
analyse the random variable $S = \sum_{x \in V}\pi(x)\xi(x)$, where $\pi$ is
the stationary distribution of the transition matrix $P$, and $\xi \in \{0,1\}$
is sampled from $\Gamma$. Allowing $P$ and $p$ to be functions of the size
$|V|$, we show that, under appropriate conditions, a normalised version of $S$
converges to a Gaussian random variable. We provide further analysis of the
noisy voter model on a variety of graphs including the complete graph, cycle,
torus and hypercube.
