To aid the understanding of the dynamics of a Markov chain it is useful to draw a diagram which indicates all the possible states of the chain, with arrows denoting the possible transitions. An arrow joins state to state if ; the precise values of the probabilities are irrelevant. If a state remains in itself with positive probability, then we simply draw a self-directed arrow.
Draw diagrams for the following Markov chains. Each chain can take the values .
(i)
(ii)
(iii)
(iv)
We say that leads to and write if
This is equivalent to saying that it is possible to get from to in some number of steps. We say that communicates with and write if both and .
The relation is an equivalence relation on the state space. It therefore partitions the state space into communicating classes.
Note that, for disjoint sets and , iff there exists a sequence of states with and for which . From this it follows that and implies . Also, for any state . Clearly implies . So is an equivalence relation and the equivalence classes partition the state space. ∎
A communicating class is closed if, for all , implies that . A closed class is therefore one from which there is no escape. A state is absorbing if is a closed class.
Identify the communicating classes for each of the Markov chains in 4.2.2. Which are closed?
. Closed.
and . Both closed.
and . Only is closed.
. Closed.
A Markov chain is irreducible if it has a single communicating class i.e. for all states and . Otherwise the chain is reducible.
Classify the the Markov chains in 4.2.2 as either irreducible or reducible.
are irreducible, and are reducible.