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Selected Examples |

Figure 9.12 shows one way to model an MMPP.
The process labeled "Markov-modulated Poisson Process" samples
from an MMPP distribution and sets the value of the parameter
lambda, the mean inter-arrival time for an exponential random
variable in the
Sampler labeled "MMPP Arrivals.".
In the upper process, lambda is given the values 10, .1, and 1
based on the state of a Markov chain.
The state is changed in the Modifier
components labeled "state."
Each has a conditional component driven
by an observation of a uniform random variable.
So, for a given state, the state is changed to the next state and the
value of lambda is chosen for the **MMPP Arrivals** Sampler.
The selected lambda is set in the **MMPP Arrivals** Sampler,
and the process is delayed for
an exponential amount of time whose parameter is state dependent.
The transaction then goes to a switch that routes based on the
state for the next state change.

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