Check Markov chain for reducibility
tf = isreducible(mc)
Consider this three-state transition matrix.
Create the Markov chain that is characterized by the transition matrix P.
P = [0.5 0.5 0; 0.5 0.5 0; 0 0 1]; mc = dtmc(P);
Determine whether the Markov chain is reducible.
ans = logical 1
1 indicates that
mc is reducible.
Visually confirm the reducibility of the Markov chain by plotting its digraph.
Two independent chains appear in the figure. This result indicates that you can analyze the two chains separately.
tf— Reducibility flag
Reducibility flag, returned as
mc is a reducible Markov chain and
The Markov chain
mc is irreducible if every state is
reachable from every other state in at most n - 1 steps,
where n is the number of states
mc.NumStates). This result is equivalent to
Z)n - 1
containing all positive elements. I is the
n-by-n identity matrix and
> 0), for all i,j, which is the
zero-pattern matrix of the transition matrix P
mc.P) . To
isreducible computes this matrix
By the Perron-Frobenius Theorem , irreducible Markov chains have unique stationary distributions. Unichains, which consist of a single recurrent class plus transient classes, also have unique stationary distributions (with zero probability mass in the transient classes). Reducible chains with multiple recurrent classes have stationary distributions that depend on the initial distribution.
 Gallager, R.G. Stochastic Processes: Theory for Applications. Cambridge, UK: Cambridge University Press, 2013.
 Horn, R. and C. R. Johnson. Matrix Analysis. Cambridge, UK: Cambridge University Press, 1985.