Gregg said:
Homework Statement
find the stationary distribtion of ##\left(
\begin{array}{ccccc}
\frac{1}{2} & \frac{1}{2} & 0 & 0 & 0 \\
\frac{1}{3} & \frac{2}{3} & 0 & 0 & 0 \\
0 & \frac{1}{3} & \frac{2}{3} & 0 & 0 \\
0 & 0 & 0 & 0 & 1 \\
0 & 0 & 0 & 1 & 0
\end{array}
\right)##
Homework Equations
##\pi S = \pi ##
The Attempt at a Solution
So I think the definition is this
##\left(
\begin{array}{ccccc}
\pi _1 & \pi _2 & \pi _3 & \pi _4 & \pi _5
\end{array}
\right)\left(
\begin{array}{ccccc}
\frac{1}{2} & \frac{1}{2} & 0 & 0 & 0 \\
\frac{1}{3} & \frac{2}{3} & 0 & 0 & 0 \\
0 & \frac{1}{3} & \frac{2}{3} & 0 & 0 \\
0 & 0 & 0 & 0 & 1 \\
0 & 0 & 0 & 1 & 0
\end{array}
\right)= \left(
\begin{array}{c}
\pi _1 \\
\pi _2 \\
\pi _3 \\
\text{}_{\pi _4} \\
\pi _5
\end{array}
\right)##
I get simulataneous equations... (I've just realized I was doing matrix multiplication the wrong way round)
##\frac{\pi_1}{2} + \frac{\pi_2}{3} = \pi_1 ##
##\frac{\pi_1}{2} + \frac{2\pi_2}{3} = \pi_2 ##
##\frac{\pi_3}{3} = \pi_3 ##
##\pi_5 = \pi_4 ##
##\pi_4 = \pi_5 ##
and with the extra condition
## \sum_i \pi_i = 1## *
This reduces to 3 equations with 4 unknowns.
## \pi_1/2=\pi_2/3 ##
## \pi_3=0##
##\pi_4=\pi_5##
and using * :
##\pi_1 +3\pi_1/2+2\pi_5 = 1##
This gives me
##\vec{\pi}=\left(
\begin{array}{c}
\pi _1 \\
3\frac{\pi _1}{2} \\
0 \\
\frac{1}{2}-5\frac{\pi _1}{2} \\
\frac{1}{2}-5\frac{\pi _1}{2}
\end{array}
\right)##
I am unsure how to find ##\pi_1##
##0 <= \pi_1 <=2 ## all seem to work fine. Is this the stationary distribution, an interval of allowed distributions? I thought they were unique.
In this problem there is
not a uniquely-defined stationary distribution. You can see why, by looking at the one-step transition matrix S. Note that if we start in state 4 or 5 we remain forever in those states, and we flip-flop back and forth between them. If we start in state 3, in the first step we either stay in state 3 or move to state 2. If we are in states 1 or 2 we stay there forever. However, from states 1,2 or 3 we cannot reach state 4 and 5, and vice-versa. Effectively, the state-space splits into two non-communicating parts, and the different steady-state vectors correspond to those two parts. For example, one distribution would be \pi_1 = \pi_2 = \pi_3 = 0, \pi_4 = \pi_5 = 1/2, corresponding to starting in states 4 or 5 and then spending half the time in each over the long run. Another distribution would have \pi_1, \pi_2 > 0, \pi_3 = \pi_4 = \pi_5 = 0, corresponding to starting in state 1,2 or 3. Note that state 3 is transient, so if we start in state 3 we leave it eventually with probability 1; that is why \pi_3 = 0. Note that we can make these conclusions without doing any calculations!
The only remaining issue is to get \pi_1, \pi_2 when we start from states 1, 2 or 3. This would be done by solving the simple 2-state case, using just rows 1-2 and columns 1-2 of S. As you note, these give
\pi_1 = (1/2)\pi_1 + (1/3)\pi_2 \Longrightarrow \pi_2 = (3/2)\pi_1. Since \pi_1 + \pi_2 = 1, we get \pi_1 = 2/5, \; \pi_2 = 3/5.
The two "basic" steady-state distributions are \Pi_1 = (2/5,3/5,0,0,0) \text{ and } \Pi_2 = (0,0,0,1/2,1/2). The most general steady-state distribution has the form
\Pi = a \Pi_1 + (1-a) \Pi_2, where a \in [0,1] is the probability we start in states 1, 2 or 3 (and 1-a = probability we start in states 4 or 5).
RGV