Expected time between successive visits to state $i$ in a Markov chain conditionally on no visit to state $j$ Say I have a Markov chain $\{X_n: n \geq 1\}$ with state space $E = \{1,2,3,4,5\}$ and transition matrix,
$$ P = \begin{bmatrix} 0 & 1/2 & 1/2 & 0 & 0 \\\ 1/2 & 0 & 1/2 & 0 & 0 \\\ 1/4 & 1/4 & 0 & 1/4 & 1/4 \\\ 0 & 0 & 1/2 & 0 & 1/2 \\\ 0 & 0 & 0 & 1/2 & 1/2\ \end{bmatrix} $$
With a stationary distribution,
$$ \pi^T =  [1/6 \quad 1/6 \quad 1/3 \quad 1/6 \quad 1/6]$$
Assuming that $X_1 = 1$, it is easy to use this information to calculate the expected number of transitions between successive visits to state 1 (the answer is $1/\pi_1 = 6$}.
What is not clear to me, however, is how to calculate the expected number of transitions between successive visits to state 1 conditional on the fact that state 5 is never visited.
Two potential approaches to answer this are:
1) Constructing the Markov chain graph without state 5, recalculating the transition matrix and the stationary distribution. This approach yields a transition matrix of
$$ P = \begin{bmatrix} 0 & 1/2 & 1/2 & 0 \\\ 1/2 & 0 & 1/2 & 0 \\\ 1/3 & 1/3 & 0 & 1/3 \\\ 0 & 0 & 1 & 0\ \end{bmatrix} $$
a stationary distribution of
$$ \pi^T =  [1/4 \quad 1/4 \quad 3/8 \quad 1/8]$$
and an answer of 4.
2) Calculating the answer as $(1/\pi_1)$ after conditioning the stationary distribution to account for the fact that state $5$ is not visited. In other words, we normalize $\pi_1 \ldots \pi_4$ by a factor of $1 - \pi_5$ to account for the fact that state $5$ is not visited. This yields a stationary distribution
$$ \pi^T =  [1/5 \quad 1/5 \quad 2/5 \quad 1/5 \quad 0]$$
and an answer of $5$. 
Some friends have argued that approach #1 is the correct approach, but cannot explain why approach #2 is wrong. 
Any insight is appreciated.
 A: The reason number 2 is wrong is that each number is predicated upon being able to either go to or come from 5 in each state, which does not uniformly affect each of the states. The degree to which each state's steady state varies depends upon the column corresponding to 5 in the transition matrix, which is not the same for each.
A: Well, I don't even think the first approach is correct. Notice that in the original MC, the expected number of jumps between to two visits in 1 is 1/$\pi_1=6$, and so is it for state 5. If the first approach is correct, then on average, there are 4 visits to state 1 between two successive visits to 5, which is not intuitively correct. In fact, the desired answer should be 1, since it is proportional to the stationary distribution, in particular, let $x(1,5)$ be the number of visits to state 1 before first return to 5 given that initially the
Markov chain was in state 5, we have $\frac{x(1,5)}{x(5,5)} = \frac{\pi_1}{\pi_5}$, (this can be shown by proving that $x(\cdot,5)$ satisfies $x(\cdot,5) = x(\cdot,5)P$), and notice that $x(5,5)=1$, we have $x(1,5)=1$.
A: I found this document which support Jikai's comment. Here is a link. In this document, it tells that 'the ratio of two limiting state probabilities represents the expected number of visits to state j between two successive visits to i which is $\pi_j/\pi_i$'. So for your problem, the answer is that 1/$\pi_1$ - $\pi_5/\pi_1$ which is '1/(1/9) - (1/3)/(1/9) = 6'.
the stationary distribution i got is $\pi=[1/9, 1/9, 2/9, 2/9, 1/3]$ which is different from your expression. The $\pi_3$ is not satisfied in your result.
hope this helps.
