How the Ornstein–Uhlenbeck process can be considered as the continuous-time analogue of the discrete-time AR(1) process? Wikipedia says

The Ornstein–Uhlenbeck process can also be considered as the continuous-time analogue of the discrete-time AR(1) process.

I was wondering how the Ornstein–Uhlenbeck process can be considered as the continuous-time analogue of the discrete-time AR(1) process?
An Ornstein–Uhlenbeck process, $x_t$, satisfies the following stochastic differential equation:
$$
    dx_t = \theta (\mu-x_t)\,dt + \sigma\, dW_t
$$
where $\theta > 0, \mu$ and $\sigma > 0$ are parameters and $W_t$ denotes the Wiener process.
The $AR(p)$ model, i.e. an autoregressive model of order $p$, is defined as
$$
    X_t = c + \sum_{i=1}^p \varphi_i X_{t-i}+ \varepsilon_t \,
$$
where $\varphi_1, \ldots, \varphi_p$ are the parameters of the model, $c$ is a constant, and $\varepsilon_t$ is white noise. 
Thanks and regards!
 A: Should be a comment on Ilya's answer but I don't have enough reputation -- 
Although the other answer does show how the AR process is equivalent to the OU process, keep in mind that the Euler-Maruyama discretization is just an approximation. In order to derive the exact relationship between the two models, you would have to integrate the OU process from time t to t+1, and then derive the various relationships between the parameters. Luckily there is a closed form solution to the OU SDE, so this is not too difficult.
EDIT: you could also use the Milstein Discretization, which is a more accurate approximation because it includes the convexity term from Ito's lemma. But it is still easier than the exact solution.
A: In case $p=1$ you have
$$
  x_{k+1} = c+a x_k + b\varepsilon_k
$$
so that if you put $c = \theta\mu\Delta t$, $a = -\theta\Delta t$ and $b = \sigma\sqrt{\Delta t}$ you get
$$
  x_{k+1} = \theta(\mu - x_k)\Delta t + \sigma \varepsilon_k\sqrt{\Delta t}
$$
which is exactly an Euler-Maryuama discretization of OU at times $(k\Delta t)_{k\in \Bbb N_0}$.
A: Rather than using an approximation (like Euler-Maryuama), one can just sample* the continuous-time solution:
$$x(t)=x(0)\,e^{-\theta t}+\mu \,(1-e^{-\theta t})+\sigma \int _{0}^{t}e^{-\theta (t-s)}\,dW_{s}.\,
$$
Writing $X_k=x(k\,\Delta t)$, the sampled process is described by the AR(1) process:
$$
  X_{k+1} = c + \varphi \,X_k + \varepsilon_k
$$
where:
\begin{align}
X_0&=x(0),\\
\varphi &= e^{-\theta \Delta t},\\
c &= (1-\varphi)\mu, \\
\epsilon_k &\sim \mathcal{N}\Big(0,\frac{1}{2\theta}\sigma^2\big(1-e^{-2 \theta \Delta t}\big)\Big).
\end{align}
* I mean sample, not sample 
