# A sufficient condition $p(\theta)$ to be the stationary distribution is the reversibility

I was reading my notes, it says that:

''A sufficient condition $p(\theta)$ to be the stationary distribution is the reversibility: $$\sum_{\theta}p(\theta)p(\theta^*|\theta)=\sum_{\theta}p(\theta^*)p(\theta|\theta^*)=p(\theta^*)\sum_{\theta}p(\theta|\theta^*)=p(\theta^*).''$$ I was wondering what does the reversibility mean here, since this expression is trivially true for any $p(\theta)$. And why the reversibility is a sufficient condition that makes $p(\theta)$ a stationary distribution? Since by definition $p(\theta)$ needs to satisfy $p(\theta)=Ap(\theta)$ where $A$ is a transition matrix (Markov Chain).

A discrete Markov chain $\theta_1, \theta_2, \ldots$ with transition pmf $k(\theta_{t+1} \mid \theta_t)$ is reversible if joint pmfs for two consecutive time points are "symmetric", or in other words $$p(\theta_{t+1} = a, \theta_{t} =b) = p(\theta_{t+1} = b, \theta_{t} =a)$$ for any $a,b$ in the state space. You can think of it as the probability of starting in $b$ and going to $a$ is the same as the probability of starting in $a$ and going to $b$. The order doesn't matter.
When we write it with the transition pmf and stationary distribution $\pi_{\theta}(\cdot )$ we get something closer to your notation: $$k(a \mid b) \pi(b) = k(b \mid a)\pi(a).$$
Any measure $\pi$ that satisfies reversibility must also satisfy stationarity. This can be seen if we sum both sides over all possible values of $a$: $$\pi(b) = \sum_{a} k(b \mid a) \pi(a).$$