What is the difference between "limiting" and "stationary" distributions? I'm doing a question on Markov chains and the last two parts say this:

  
*
  
*Does this Markov chain possess a limiting distribution. If your answer is "yes", find the limiting distribution. If your answer is "no", explain why.
  
*Does this Markov chain possess a stationary distribution. If your answer is "yes", find the stationary distribution. If your answer is "no", explain why.
  

What is the difference? Earlier, I thought the limiting distribution was when you work it out using $P = CA^n C^{-1}$ but this is the $n$'th step transition matrix. They calculated the limiting distribution using $\Pi = \Pi P$, which I thought was the stationary distribution.
Which is which then?
 A: From An Introduction to Stochastic Modeling by Pinsky and Karlin (2011):

A limiting distribution, when it exists, is always a stationary distribution, but the converse is not true. There may exist a stationary distribution but no limiting distribution. For example, there is no limiting distribution for the periodic Markov chain whose transition probability matrix is
  $$
\mathbf{P}=\left\|\begin{matrix}0 & 1\\1 & 0\end{matrix}\right\|
$$
  but $\pi=\left(\frac{1}{2},\frac{1}{2}\right)$ is a stationary distribution, since
  $$
\left(\frac{1}{2},\frac{1}{2}\right)\left\|\begin{matrix}0 & 1\\1 & 0\end{matrix}\right\|=\left(\frac{1}{2},\frac{1}{2}\right)
$$ (p. 205).

In a prior section, they had already defined a "limiting probability distribution" $\pi$ by

$$\lim_{n\rightarrow\infty}P_{ij}^{(n)}=\pi_j~\mathrm{for}~j=0,1,\dots,N$$

and equivalently

$$\lim_{n\rightarrow\infty}\operatorname{Pr}\{X_n=j|X_0=i\}=\pi_j>0~\mathrm{for}~j=0,1,\dots,N$$ (p. 165).

The example above oscillates deterministically, and so fails to have a limit in the same way that the sequence $\{1,0,1,0,1,\dots\}$ fails to have a limit.

They state that a regular Markov chain (in which all the n-step transition probabilities are positive) always has a limiting distribution, and prove that it must be the unique nonnegative solution to

$$\pi_j=\sum_{k=0}^N\pi_kP_{kj},~~j=0,1,\dots,N,\\
\sum_{k=0}^N\pi_k=1$$ (p. 168)

Then on the same page as the example, they write

Any set $(\pi_i)_{i=0}^{\infty}$ satisfying (4.27) is called a stationary probability distribution of the Markov chain. The term "stationary" derives from the property that a Markov chain started according to a stationary distribution will follow this distribution at all points of time. Formally, if $\operatorname{Pr}\{X_0=i\}=\pi_i$, then $\operatorname{Pr}\{X_n=i\}=\pi_i$ for all $n=1,2,\dots$.

where (4.27) is the set of equations

$$\pi_i \geq 0, \sum_{i=0}^{\infty} \pi_i=1,~\mathrm{and}~\pi_j = \sum_{i=0}^{\infty} \pi_iP_{ij}.$$

which is precisely the same stationarity condition as above, except now with an infinite number of states.
With this definition of stationarity, the statement on page 168 can be retroactively restated as:


*

*The limiting distribution of a regular Markov chain is a stationary distribution.

*If the limiting distribution of a Markov chain is a stationary distribution, then the stationary distribution is unique.

A: A stationary distribution is such a distribution $\pi$ that if the distribution over states at step $k$ is $\pi$, then also the distribution over states at step $k+1$ is $\pi$. That is, 
\begin{equation}
\pi = \pi P.
\end{equation}
A limiting distribution is such a distribution $\pi$ that no matter what the initial distribution is, the distribution over states converges to $\pi$ as the number of steps goes to infinity:
\begin{equation}
\lim_{k\rightarrow \infty} \pi^{(0)} P^k = \pi,
\end{equation}
independent of $\pi^{(0)}$.
For example, let us consider a Markov chain whose two states are the sides of a coin, $\{heads, tails\}$. Each step consists of turning the coin upside down (with probability 1). Note that when we compute the state distributions, they are not conditional on previous steps, i.e., the guy who computes the probabilities does not see the coin. So, the transition matrix is 
\begin{equation}
P = \begin{pmatrix} 0 & 1 \\ 1 & 0 \end{pmatrix}.
\end{equation}
If we first initialize the coin by flipping it randomly ($\pi^{(0)} = \begin{pmatrix}0.5 & 0.5\end{pmatrix}$), then also all subsequent time steps follow this distribution. (If you flip a fair coin, and then turn it upside down, the probability of heads is still $0.5$). Thus, $\begin{pmatrix} 0.5 & 0.5 \end{pmatrix}$ is a stationary distribution for this Markov chain. 
However, this chain does not have a limiting distribution: suppose we initialize the coin so that it is heads with probability $2/3$. Then, as all subsequent states are determined by the initial state, after an even number of steps, the state is heads with probability $2/3$ and after an odd number of steps the state is heads with probability $1/3$. This holds no matter how many steps are taken, thus the distribution over states has no limit.
Now, let us modify the process so that at each step, one does not necessarily turn the coin. Instead, one throws a die, and if the result is $6$, the coin is left as is. This Markov chain has transition matrix
\begin{equation}
P = \begin{pmatrix} 1/6 & 5/6 \\ 5/6 & 1/6 \end{pmatrix}.
\end{equation}
Without going over the math, I will point out that this process will 'forget' the initial state due to randomly omitting the turn. After a huge amount of steps, the probability of heads will be close to $0.5$, even if we know how the coin was initialized. Thus, this chain has the limiting distribution $\begin{pmatrix} 0.5 & 0.5 \end{pmatrix}$.
A: Putting notation aside, the word "stationary" means "once you get there, you will stay there"; while the word "limiting" implies "you will eventually get there if you go far enough". Just thought this might be helpful.
