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Markov Chains and Steady State
Markov Chains and Steady State
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conceptual
A finite, irreducible, aperiodic Markov chain has a unique stationary distribution
π
satisfying
π
P
=
π
. What does irreducibility require?
Hide and think first
A.
The chain returns to every state in exactly one step
B.
Every state has the same stationary probability
C.
Every state can be reached from every other state in a finite number of steps
D.
The transition matrix is symmetric
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