2. A bank classifies loans as paid in full(F), in good standing (G), in arrears (A), or as a bad debt (B). Loans move between the categories according to the transition matrix: P = F G A B F 1 0 0 0 G .1 .8 .1 0 A .1 .4 .4 .1 B 0 0 0 1 What fraction of loans in good standing are eventually paid in full? (That is, starting at state G, what is the probability that the chain eventually visits F?)

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• General notation for Markov chains: P(A) is the probability of the event A when the Markov
chain starts in state x, Pμ(A) the probability when the initial state is random with distribution μ.
Ty = min{n ≥ 1 : X₂ = y} is the first time after 0 that the chain visits state y. Px,y = Px(Ty < ∞) . Ny
is the number of visits to state y after time 0.
2.
A bank classifies loans as paid in full(F), in good standing (G), in arrears (A), or as a bad debt
(B). Loans move between the categories according to the transition matrix:
P =
F
G A B
F
1 0 0 0
G
.1 .8 .1
0
A
.1
.4 .4 .1
B 0
0 0 1
What fraction of loans in good standing are eventually paid in full? (That is, starting at state G, what
is the probability that the chain eventually visits F?)
Transcribed Image Text:• General notation for Markov chains: P(A) is the probability of the event A when the Markov chain starts in state x, Pμ(A) the probability when the initial state is random with distribution μ. Ty = min{n ≥ 1 : X₂ = y} is the first time after 0 that the chain visits state y. Px,y = Px(Ty < ∞) . Ny is the number of visits to state y after time 0. 2. A bank classifies loans as paid in full(F), in good standing (G), in arrears (A), or as a bad debt (B). Loans move between the categories according to the transition matrix: P = F G A B F 1 0 0 0 G .1 .8 .1 0 A .1 .4 .4 .1 B 0 0 0 1 What fraction of loans in good standing are eventually paid in full? (That is, starting at state G, what is the probability that the chain eventually visits F?)
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