assignment9

The steady state probabilities pi for the markov

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Unformatted text preview: s the ﬁrst holding interval (U ). Thus, E[V |X0 = i, X1 = i + 1] = E[U1 |X0 = i, X1 = i + 1] = 1/(λ + µ) Conditional on {X0 = i, Xi+1 = i − 1}, we know that the ﬁrst transition is a departure.So the time until the ﬁrst arrival is sum of the time for ﬁrst transition (i.e., a departure) and the time until the next arrival. The second term is exponentially distributed with rate λ, so we have: 5 E[V |X0 = i, X1 = i − 1] = E[U1 |X0 = i, X1 = i − 1] + E[V |X1 = i − 1] = 1 1 + λ+µ λ d) Using the total expectation lemma, we have: E[V |X0 = i] = E[V |X0 = i, X1 = i + 1]Pr {X1 = i + 1|X0 = i} + E[V |X0 = i, X1 = i − 1]Pr {X1 = i − 1|X0 = i} � � 1 λ 1 1 µ 1 = + + = λ+µλ+µ λ+µ λ λ+µ λ Since this is true for any choice of i > 0, and it was assumed that X0 = i, for i > 0, E[V ] = 1/λ. Exercise 6.2: The transition diagram for the embedded chain is: �� �� ��0��� 1 3/5 �� � �� ��1��� 2/5 3/5 �� � �� ��2��� 2/5 3/5 �� � �� ��3��� 2/5 3/5 �� � �� ��4��� 2/5 � ... 3/5 3 a) The steady state probabilities satisfy π0 = 3 π1 , 2 πi−1 = 5 πi for i ≥ 2. Iterating on 5 5 these equations, � �i−1 �� 2 5 2 i−1 π1 = π0 , for i ≥ 1 3 33 ⎤ ⎡ � 5 � 2 �i−1 � ⎦ = 6π0 1= πi = π0 ⎣1 + 33 2 πi...
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This document was uploaded on 03/19/2014 for the course EECS 6.262 at MIT.

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