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Transcript
10.2
Quick Notes
Regular Markov Chains
1) If P is a transition matrix, then Pn gives the probability of moving from state I to state j in n steps (observations,
stages).
2) A Markov Chain is regular if, for some (any) power of the transition matrix P, all entries are positive (no 0's)
(Note: if Pn has all positive entries, the every consecutive power of P after that will too)
3) Long term behavior of a regular Markov chain.
a) As n gets large, Pn approaches a fixed matrix T (Pn  T)
b) The rows of T are identical (row probability vector t)
c) tP = t
d) if v(0) is the initial probability distribution, then as n gets large, v(n)  t
Examples.
1) If a Markov chain has the following transition matrix, would it be regular?
1
a) .5

0
.5

0
b) .5

1
.5

.9
c) .5

.1
.5

2) There are two insurance companies in town; BigMega inc. and Mom's insurance. All people in town have one or
the other. Every year, 5% of BigMega's customers switch to Mom's (the others stay), and 3% of Mom's customers
move to BigMega (the others stay).
a) Find the initial probability distribution.
b) Find the transition matrix.
c) Eventually, in the long run, what percentage of the customers will each have?