Consider the directed probability graph, with 4 vertices (0, 1, 2, 3) represented by the following adjacency matrix P:
[1/3, 1/3, 0, 1/3]
[1/3, 1/3, 1/3, 0]
[ 0, 0, 0, 0]
[ 0, 0, 0, 0]
The edges represent transition probabilities between vertices. The edges are [(0,0), (0,1), (0,3), (1,0), (1,1), (1,2)] each with transition probability 1/3. There are two self loops (0,0) and (1,1), and a cycle created by edges (0,1) and (1,0).
For such (and possibly bigger and complex) graphs (with self loops and cycles, and hence possibly infinite number of possible paths), how does one go about calculating the total probability of all possible cycles that start at vertex 0 and end at vertex 0?
I've calculated this for 3-vertex graphs using geometric series. For instance, which comes out to:
P(0,2) * [ P(2,0) + P(1,2)*P(2,0) ] + P(0,1) * [ P(1,0) + P(1,2)*P(2,0) ]
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[ 1 - P(1,2) * P(2,1) ]
Which is basically the sum of probabilities of simple paths, divided by some factor which does correction for the loop made by edges (1,2) and (2,1).
With this calculation, the results for 3-vertex graphs tally with the results of the problem I'm trying to solve. I'm not sure how to scale this to bigger graphs.
PS: This is in continuation to this question and the accepted answer, in which a parameter Pr(Cii(0)|G needs to be calculated.